Guest Post by Willis Eschenbach
[see Update at the end of this post]
I got to thinking about the (non) adjustment of the GISS temperature data for the Urban Heat Island effect, and it reminded me that I had once looked briefly at Anchorage, Alaska in that regard. So I thought I’d take a fresh look. I used the GISS (NASA) temperature data available here.
Given my experience with the Darwin, Australia records, I looked at the “homogenization adjustment”. According to GISS:
The goal of the homogenization effort is to avoid any impact (warming or cooling) of the changing environment that some stations experienced by changing the long term trend of any non-rural station to match the long term trend of their rural neighbors, while retaining the short term monthly and annual variations.
Here’s how the Anchorage data has been homogenized. Figure 1 shows the difference between the Anchorage data before and after homogenization:
Figure 1. Homogenization adjustments made by GISS to the Anchorage, Alaska urban temperature record (red stepped line, left scale) and Anchorage population (orange curve, right scale)
Now, I suppose that this is vaguely reasonable. At least it is in the right direction, reducing the apparent warming. I say “vaguely reasonable” because this adjustment is supposed to take care of “UHI”, the Urban Heat Island effect. As most everyone has experienced driving into any city, the city is usually warmer than the surrounding countryside. UHI is the result of increasing population, with the accompanying changes around the temperature station. More buildings, more roads, more cars, more parking lots, all of these raise the temperature, forming a heat “island” around the city. The larger the population of the city, the greater the UHI.
But here’s the problem. As Fig. 1 shows, until World War II, Anchorage was a very sleepy village of a few thousand. Since then the population has skyrocketed. But the homogeneity adjustment does not match this in any sense. The homogeneity adjustment is a straight line (albeit one with steps …why steps? … but I digress). The adjustment starts way back in 1926 … why would the 1926 Anchorage temperature need any adjustment at all? And how does this adjust for UHI?
Intrigued by this oddity, I looked at the nearest rural station, which is Matanuska. It is only about 35 miles (60 km) from Anchorage, as shown in Figure 2.
Figure 2. Anchorage (urban) and Matanuska (rural) temperature stations.
Matanuska is clearly in the same climatological zone as Anchorage. This is verified by the correlation between the two records, which is about 0.9. So it would be one of the nearby rural stations used to homogenize Anchorage.
Now, according to GISS the homogeneity adjustments are designed to adjust the urban stations like Anchorage so that they more closely match the rural stations like Matanuska. Imagine my surprise when I calculated the homogeneity adjustment to Matanuska, shown in Figure 3.
Figure 3. Homogenization adjustments made by GISS to the Matanuska, Alaska rural temperature record.
Say what? What could possibly justify that kind of adjustment, seven tenths of a degree? The early part of the record is adjusted to show less warming. Then from 1973 to 1989, Matanuska is adjusted to warm at a feverish rate of 4.4 degrees per century … but Matanuska is a RURAL station. Since GISS says that the homogenization effort is designed to change the “long term trend of any non-rural station to match the long term trend of their rural neighbors”, why is Matanuska being adjusted at all?
Not sure what I can say about that, except that I don’t understand it in the slightest. My guess is that what has happened is that a faulty computer program has been applied to fudge the record of every temperature station on the planet. The results have then been used without the slightest attempt at quality control.
Yes, I know it’s a big job to look at thousands of stations to see what the computer program has done to each and every one of them … but if you are not willing to make sure that your hotrod whizbang computer program actually works for each and every station, you should not be in charge of homogenizing milk, much less temperatures.
The justification that is always given for these adjustments is that they must be right because the global average of the GISS adjusted dataset (roughly) matches the GHCN adjusted dataset, which (roughly) matches the CRU adjusted dataset.
Sorry, I don’t find that convincing in the slightest. All three have been shown to have errors. All that shows is that their errors roughly match, which is meaningless. We need to throw all of these “adjusted datasets” in the trash can and start over.
As the Romans used to say “falsus in unum, falsus in omnibus”, which means “false in one thing, false in everything”. Do we know that everything is false? Absolutely not … but given egregious oddities like this one, we have absolutely no reason to believe that they are true either.
Since people are asking us to bet billions on this dataset, we need more than a “well, it’s kinda like the other datasets that contain known errors” to justify their calculations. NASA is not doing the job we are paying them to do. Why should citizen scientists like myself have to dig out these oddities? The adjustments for each station should be published and graphed. Every single change in the data should be explained and justified. The computer code should be published and verified.
Until they get off their dead … … armchairs and do the work they are paid to do, we can place no credence in their claims of temperature changes. They may be right … but given their egregious errors, we have no reason to believe that, and certainly no reason to spend billions of dollars based on their claims.
[Update – Alaska Climate Research Center releases new figures]
I have mentioned the effect of the Pacific Decadal Oscillation (PDO) below. The Alaska Climate Research Center have just released their update to the Alaska data. Here’s that information:
Figure 4. Alaska Temperature Average from First Order Observing Stations
In the Alaska Climate Research Center data, you can clearly see the 1976 shift of the PDO from the cool to the warm phase, and the recent return to the cool phase. Unsurprisingly, the rise in the Alaska temperatures (typically shown with a continuously rising straight trend line through all the data) have been cited over and over as “proof” that the Arctic is warming. However, the reality is a fairly constant temperature from 1949-1975, a huge step change 1975-1976, and a fairly constant temperature from 1976 until the recent drop. Here’s how the IPCC Fourth Assessment Report interprets these numbers …
Figure 5. How the IPCC spins the data.
SOURCE: (IPCC FAR WG1 Chapter 9, p. 695)
As you can see, they have played fast and loose with the facts. They have averaged the information into decade long blocks 1955-1965, 1965-1975, 1975-1985 etc. This totally obsures the 1975-1976 jump. It also gives a false impression of the post-1980 situation, falsely showing purported continuing warming post 1980. Finally, they have used “adjusted data” (an oxymoron if there ever was one). As you can see from Fig. 4 above, this is merely global warming propaganda. People have asked why I say the Alaska data is “fudged” … that’s a good example of why.
Thanks, Willis, for keeping attention focused on fudged climate data.
“Figures don’t lie, but liars sure figure!”
With kind regards,
Oliver K. Manuel
Again we see that actual temperature readings are discarded in favor of altered readings. The public is never informed. There appears to be absolutely no rational reason to alter the readings. This is a prime example, but not unprecedented.
Nice one, Willis. Now i’m eager to see the first AGW specimen and the excuse it brings us.
There needs to be a full investigation into the adjustments of every weather station used in the temperature reconstructions. This should include an analysis of exactly how the data is being adjusted and a short paper as to what each adjustment does and why it is necessary.
It was, for example, recently admitted in the New Zealand Parliament that without adjustments, the temperature record for New Zealand shows no warming – the 0.9 degrees C warming that is shown in the official record is entirely as a result of the adjustments.
http://www.parliament.nz/en-NZ/PB/Business/QOA/8/8/e/49HansQ_20100217_00000009-9-National-Institute-of-Water-and-Atmospheric.htm
It may be that the adjustments are valid but there is no schedule of them so nobody can see exactly what they do and why.
It may be expensive to do but the total cost is chicken-feed compared to the billions being spent on climate change mitigation.
I think you forgot the minus signs in Figure 1, or have the terms reversed. If the adjusted temperature is larger than unadjusted, the correction would result in raising the earlier temperatures, not lowering. Figure 3 looks strange, since the correction used would make the dip between 1920 to 1990 deeper, and this is probably backwards. I think you have a sign error in both.
What is needed is a surface station data effort.
Yes, first the data, then the “science”. Thanks!
Kinda started slow, but really picked up speed towards the end there.
You were on to something with: “Not sure what I can say about that, except that I don’t understand it in the slightest.” That seems like a logical place to stop. You raise a question, maybe somebody addresses it, everybody goes home happy.
Unfortunately you go on to assume all sorts of things about what it must mean, and end with a rousing course of “all the measurements are wrong.” Typically, you cannot even bring yourself to accurately describe the evidence that the measurements are not wrong. The improbability of the exact same errors showing up all over the world in multiple data sets collected by different methods and different people you dismiss as “meaningless,” which it most certainly is not.
No, you were dead on: you don’t understand what’s going on in the slightest.
The same here in NZ, a classic was that the trend of raw temperature data at one station was negative, after adjusting for no known reason the trend was positive!.
There you go again, Anthony, focusing on the “nitty-gritty of measurement,” the “small technicalities” that “don’t matter” in“an epic game of nitpicking” and yet again “zeroing in on minor technical issues while ignoring the massive and converging lines of evidence.”
Leonard Weinstein (15:30:54)
Leonard, I believe the signs are correct. Since the early years of the Anchorage record are adjusted to be warmer than the later years, this reduces the apparent warming. Or as I said above, “At least it is in the right direction, reducing the apparent warming.”
Thanks for checking, that’s science at work.
James S (15:29:37) :
“There needs to be a full investigation into the adjustments of every weather station used in the temperature reconstructions”
There will be.
Commonwealth of Virginia v. U.S. Environmental Protection Agency — Petition for Reconsideration of Endangerment & Cause (U.S. Court of Appeals District of Columbia)
http://www.oag.state.va.us/LEGAL_LEGIS/CourtFilings/Comm%20v%20EPA%20-%20Pet%20for%20Reconsideration%202_16_10.pdf
I think my first comment is partially wrong. The correction should have been made to later data, not earlier, but a correction of later data would be in the direction shown. However, the overall level of temperature would be wrong with where they made the correction. If this result was averaged in with other data, the overall level would be biased high, even though the direction of correction locally would give a more correct trend locally. I also suspect the magnitude of correction is much too low at later dates.
Willis got the sign right IMHO, read it like this: back in the day when Anchorage was a small village, 0.9 degrees were added.
Later when it was a big city 0 degrees were added. This is a compensation for the UHI.
Nope. Sorry. The reason that you do not understand the correction to the readings at Matanuska is that there is no verifiable reason for them.
I’m sorry, that is not quite what I meant. I meant that there is no verifiable scientific reason for them.
There is a reason. Hide The Decline.
The rural site was adjusted to make it fit the urban site, which was not properly adjusted to correct for the heat island effect at all.
Beginning in about 1955, there should have been about a 5 degree adjustment in the Anchorage temperature, to adjust for the heat island effect.
And Matanuska needed no adjustment at all, having incurred no heat island effect.
This is not homogenization. This is false data manipulation, ordered up by politicians, for the purpose of furthering their dreams of a Universal Utopian Socialist State.
To quote Commodore Edwin Peary, “find a way or fake one.”
Willis —
Very interesting!
The Anchorage adjustments appear to occur every 9 years or so on average, rather than an even 10, which in itself is a little curious.
But then the Matanuska adjustments, which are on about the same schedule until their min around 1970, suddenly increase to every 2 or 3 years. Curiouser yet!
It has been evident for some years now that Big Government offices collude in bad faith under false pretenses to promote a radical Warmist agenda increasingly divorced from reality. Manifestly, Climate Cultists’ goal is to sabotage, subvert the private-sector energy economy, ensuring that coal, oil, nuclear sources default to collectivist Statist zero, control by a fathomlessly corrupt, incompetent administrative/bureaucratic/regulatory apparat.
Cap-and-tax, EPA usurpations, have become so blatantly overt that no objective, rational observer can deny plain fact: This is not “politics as usual” but an ongoing, slow-motion coup de main. Difficult to realize, nevermind accept, individual actors are coalescing to proclaim, They Shall Not Pass.
Steve McIntyre did an audit of the GISS UHI adjustments in March 2008 after “scraping” the NASA website.
The audit shows that NASA applies an urban correction of its GISS temperature index in the wrong direction in 45% of the adjustments. Instead of eliminating the urbanization effects, these wrong way corrections makes the urban warming trends steeper. This article discusses Steve McIntyre’s audit of the GISS adjustments, with links to his original post:
http://www.friendsofscience.org/assets/documents/CorrectCorrections.pdf
I have extreme difficulty understanding, from GISS policy as related above, why any rural station would be adjusted upwards.
I assume I am missing something obvious and would dearly like to be enlightened.
OT, but the Sun’s recovery would appear to be less than stellar, ( sorry couldn’t resist ), are we still bumping along the bottom of the minimum? and if so is this cycle a little longer than quoted?
Willis, If GISS adjusted the “rural” record, then you better check the metadata.
Not sure if hansen used nightlights for alaska. Anyways the algorithm should not change a station that “classifies” as rural. So if he used nighlights
and nightlights were “dim” or “bright” then it would get adjusted.
If he used population, then the pop would have to be less than 5K in 1995.
it aint rural unless hansen says its rural. take a picture from space in 1995 to tell.
James S (15:29:37) :
There needs to be a full investigation into the adjustments of every weather station used in the temperature reconstructions. This should include an analysis of exactly how the data is being adjusted and a short paper as to what each adjustment does and why it is necessary.
No doubt the “no money for that” line will be trotted out but given the mind numbing sums that have already disappeared into the black hole that is AGW one way or another…
” Leonard Weinstein (15:30:54) :
I think you forgot the minus signs in Figure 1, or have the terms reversed. If the adjusted temperature is larger than unadjusted, the correction would result in raising the earlier temperatures, not lowering. Figure 3 looks strange, since the correction used would make the dip between 1920 to 1990 deeper, and this is probably backwards. I think you have a sign error in both.”
“falsus in unum, falsus in omnibus”?
Would it be possible to append the second graph (the one of Matanuska) to show the population growth?
Second, at what interval does the adjustment steps for Anchorage and Matanuska occur? If they’re using a population growth for the adjustment steps to account or UHI, one would assume that they occur at National Census times (i.e, every 10 years or so).
The steps for the “rise” in Matanuska seem to be every two or three years since the mid-70’s…
They falsified the data, and they are perfectly aware of it.
They hoped that their funding would be written into the law before anybody noticed. How pathetic.
Thank you, Mr. Eschenbach.
(Was the famous minstrel, Wolfram von Eschenbach, by any chance, one of your ancestors?)
Little wonder that Joe & Josie Plumber are still buying Farmer’s Almanacs every year.
Science is killing itself, worst case of murder-suicide Western Civilization has ever seen.
Maybe the Politicians will see how much fun it is and join the game.
The correct way to adjust for UHI is to lower the Urban station data.
You adjust that which is subject to increasing UHI effect, not that which is still in it’s natural state and in no way subject to anything but natural variation, which is the whole point of taking the temps.
Where’s Harry? You talents are needed. Life time employment guaranteed.
Robert (15:34:47).
OK, Robert, you tell us exactly what’s going on.
Unless, of course, your iPhone is charging on line…
“”why would the 1926 Anchorage temperature need any adjustment at all?””
To show unprecedented warming.
“”What could possibly justify that kind of adjustment, seven tenths of a degree?””
Because it’s a travesty that it no longer matched Anchorage.
“”Since people are asking us to bet billions on this dataset””
No, trillions, and a complete life overhaul.
Thanks again Willis
DirkH …
“This is a compensation for the UHI.”
Actually the only way at adjust for UHI is to lower temperatures, you would never adjust up to correct UHI …
These adjustments are pure nonsense …
Has anyone actually found an adjusted record that appears to be justified and rational ? i.e. with a real UHI adjustment ?
I have looked at the GISS before and after data for several dozen cities and I have yet to find one with a correct UHI adjustment. and by correct I mean adjusted down more today that 20 or 30 years ago … in every case the largest adjustment have always been in the past with a ladder like line of reduction climbing to present day …
also what in the world would justify the adjustments to the rural station data ? No UHI so why is the raw rural data being adjusted at all …
Willis, thank you.
This is really more of the same!
The merry fraudster team (MFT) are attempting to show a steeper increase in temps. But this is outrageous!
The MFT needed to lower the past base temps and increase the more recent temps to show a steeper increase, to attempt to prove AGW.
Thanks to Dr. Christy, amongst others, we know these are bogus temps and the earth has been much warmer in the past, without burning fossil fuels.
I wonder how well the AGW MFT sleeps at night?
Does the world have the cajones to jail the MFT?
In future, please can you show the actual numbers – original and then adjusted/manipulated – as well as the graphs.
This makes it that much more difficult for the alarmists to deny it’s happening.
…continuation for the previous post, from Wikipedia:
“…In the 1970’s relatively large numbers of newcomers to Alaska came to Anchorage, then relocated 40 miles up the Glenn Highway to the largely rural Matanuska Valley where a “Alaskan country” lifestyle pervades…”
So more than likely they built an airport or two to move this massive increase, and that’s where the temp gauge is. It appears that Matanuska may be suffering from UHI, which makes the graph of adjustments seem even stranger.
Further research shows that they actually call the area the “Matanuska-Susitna Borough”, and there is no actual town called Matanuska.
“… As of 2008, (Alaska Dept. of Labor) there were 82,515 people residing in the borough. According to the 2000 Census, the population density was 2 people per square mile (1/km²). There were 27,329 housing units at an average density of 1 per square mile (0/km²)…”
Definitely UHI…
mathman (15:49:40) :
“…This is false data manipulation, ordered up by politicians, for the purpose of furthering their dreams of a Universal Utopian Socialist State.”
LOL, but not really.
It’s amazing what you can get for your “data manipulation” buck these days.
Robert (15:34:47)
Nonsense. Read what I wrote and stop making things up. I said specifically that we don’t know if they are wrong, but we also don’t know if they are right. I said:
Your attempt to put words in my mouth is repugnant. If you have ideas, bring them out. If you have issues with what I wrote, quote what I said and tell me what’s wrong with it.
I did not say that the “exact same errors” have shown up, once again that’s your fantasy. Quote my exact words if you disagree with them, it is an unpleasant tactic to claim I said something that I did not say.
I said that when a variety of errors have been discovered in three datasets, the fact that their overall global average is somewhat similar doesn’t provide me with any comfort.
For example, when the GISS dataset first came out, it showed a smaller trend than either the CRU or the GHCN datasets. Since then, every new adjustment to the GISS dataset has increased the trend and reduced that difference, to the point where they are now much closer. Coincidence? I doubt it.
If you think that their current rough agreement means that both datasets are right, I fear I can’t help you. When datasets are constantly compared to each other and modified so they agree better and better, their final agreement is meaningless.
Finally, since you seem to be defending GISS and claiming it is accurate … why was Matanuska homogenized in the way that it was? And more to the point, why was it homogenized at all?
My guess, which I clearly identified as a guess, is computers gone wild combined with no quality control.
What’s your explanation?
Robert (15:34:47) :
Kinda started slow, but really picked up speed towards the end there.
…
No, you were dead on: you don’t understand what’s going on in the slightest
______________________________________
REPLY:
Then by all means, Robert–enlighten us. Tell us wherein you know it all and we don’t.
Give us the reason according to your version of the story.
I double triple-dog dare you!
And if you don’t then I kindly request that you refrain from such comments from now on.
BTW, what scientific credentials do you have? Put them out if you have any.
The improbability of the exact same errors showing up all over the world in multiple data sets collected by different methods and different people
it’s not errors in the data sets being collected, it’s errors in the “adjustment” of the data.
And if you have a computer program that applies the same erroneous “adjustment” to each and every raw temperature record, you have a whole world of junk.
The audit shows that NASA applies an urban correction of its GISS temperature index in the wrong direction in 45% of the adjustments. Instead of eliminating the urbanization effects, these wrong way corrections makes the urban warming trends steeper
it looks to me like they’ve got a sign reversed; and I think it’s somewhere in the year counter. They’re making cooling adjustments back in time, rather than from the point of inception forward. this is why a warm year will result in changes that make 1934 (or whatever) suddenly get “adjusted” as cooler. And the more years get added, the farther back the adjustments get made.
now, that’s just a seat-of-the-pants analysis, but I’ve been looking at numbers for a lot of years, and my gut tells me to look for a counter programming error.
these guys aren’t programmers, and they don’t check their output. And it would be an easy rookie error to make.
just my 0.02
Robert follows the same tactic every time. Probably he’s got a “Thread hijacking for dummies” app on his iPhone. Robert, could you please tell your iPhone that the greenhouse effect violates the laws of thermodynamics, could i please have the stock rebuttal for that?
Robert:
The improbability of the exact same errors showing up all over the world in multiple data sets collected by different methods and different people you dismiss as “meaningless,” which it most certainly is not.
Pay attention first, before you spout.
The errors are clearly not at the collection of the data. That would be daft.
The errors are to adjusted data, and will all be the same because the same body is applying them. Namely NASA.
Now the trickly question, Robert. How do you explain that this sort of oddity has appeared in GISS for Alaska, and also NIWA’s analysis for NZ, and in Australia too? Mere fluke?
Smacks forehead. WTF? It’s so disappointing to see how amateur these “scientists” are. Very sad. Absolutely agree that it should be possible to trace back every adjustment all the way to the raw data for each station. How hard is that?
Of course I have trouble understanding the rationale for the way these data are used. Why is it useful to average temperatures? How meaningful is an average between a daily high and a daily low? That tosses so much information from hourly (or continuous) measures. Then averaging those into a monthly or yearly average? And then average that by region and then the whole globe? Seems nuts to leave behind so much data in search of a single yearly number to plot.
My other major concern is with the value of measuring air temperatures at the surface. When the entire heat content of the atmosphere is equivalent in amount to that in the first 8 feet of the oceans, shouldn’t we be much more interested in the heat content of the oceans? Given that there is no daily fluctuation in that temperature, I would find it a much better measure.
Willis, I think the way I characterized your writing and quoted you was completely accurate. I think your overwrought and emotional response suggests my criticisms found their mark.
You said something honest — you don’t understand why they made the adjustments they made. And then you went off the deep end.
If you want your guesses to be taken seriously, you need to make a little more of an effort to figure out what is going on before you go all tinfoil-hat on the subject.
I’ll ask you again: can you accurately describe any of the arguments in favor of the temperature measurements as they are?
If you can’t imagine how you could possibly be wrong, or understand the efforts that have been made to insure the data’s accuracy, there’s no point in challenging your faith with facts.
The real science is coming in – we are not even sure there has been any global warming at all. So, the whole measurement, monitoring and analysis of global temperatures over the past 100 or so years has to be completely reviewed, and if necessary dumped if there’s insufficient accuracy to categorically say we have warmed, cooled or changed very little.
Oh, so the adjustments are hokey. But we have known that for several years already. Apparently discoveries such as these are falling in deaf ears because awareness doesn’t seem to seep beyond blogs such as this one that have been pointing these “adjustment” problems out for the past several years.
The logical thing to do would seem to be to eliminate urban stations from the record, use only rural stations, and not use any “adjustment” for Urban Heat Island. But the various groups seem to be doing the opposite, they are removing rural stations.
The whole thing is just a mess.
Gotta remember that the product these guys produce is a grid. And while a grid should tie at every single data point, every single time – its a lot easier for them to change the data when its hidden, including a newly manufactured “RHI”(Rural Heat Island) effect. Fast and loose with data to say the least.
At least ONE person (probably 2-3) at CRU was ashamed, glad for their release, glad you guys called them on it and continue to investigate. As the REAL scientists.
I don’t think charging precludes typing-up a lucid (or any other type of) response, unless, he has to have his charging-cable plugged-in in lieu of a larger-sized keyboard …
.
.
facepalm!
The “Intergovernmental Panel on Climate Change” wants data on the climate.
Knowing their agenda, do you:
1) Tell them, it’s rather chaotic.
2) Sell your soul.
henry (16:02:33)
I haven’t been able to find data on that because it is a rural station. The GHCN station metadata is listed as
42570274001 MANTANUSKA AES 61.57 -149.27 46 225R -9FLxxCO30x-9TUNDRA C
with no population given, as is usual with rural stations. From Google Earth, if those coordinates are right (61.57N, 149.27W) it is definitely rural.
The Anchorage steps are all but one at nine years, with one of ten years. The Matanuska steps downwards are about half and half seven and eight years. The upwards steps go three years, two years, three, two, three, two.
GISS doesn’t adjust for UHI by population in any case.
w.
Robert (15:59:50)
Before you start gloating, you should actually check the numbers yourself. There is no substitute for doing the math yourself. Otherwise, you just look foolish. Leonard was in error, not myself. See Willis Eschenbach (15:37:55) and Leonard Weinstein (15:40:06) and DirkH (15:42:07).
Nice try, though …
Why is there even a practice to adjust temperature data at all? I’m
a PhD scientist and work with raw data all the time. If urban heat islands and those sorts of things are having an impact on the data, then those factors should be used to describe the data during model building. You always run into danger when you “adjust” data here and there. Confusion arises over time: are you working with the raw data or adjusted data, and has it been adjusted properly. I for one always want the raw, unadulterated data to work with.
Perhaps I’m reading it incorrectly. But it appears that they are creating a stepwise drop in the temperature record from 1920 to 1970. Then they are adding a stepwise increase to the temperature records after 1970.
If so, it appears a number of things may be in play.
First, the incremental changes over time might be harder to detect, compared to isolated changes in a couple decades that may be larger.
A way to help “Hide the Decline and Incline.”
Second, it is a way to reduce the temps of the Warm Period in the 20s, 30s and 40s, perhaps to help make their record show the 1990s as the warmest decade of the century. Cooling the earlier temps helps secure their claims.
Third, by continuing the decline into the 50s to 70s, it increases the mid-century cooling. Further decline is expected anyway, so a little more might not be noticed.
Fourth, once the temps have been significantly dropped during the mid-century cooling, the dramatic warming that is added, makes their AGW Hockey-Stick-like claims that much more dramatic. That is, the difference between the lowest point in the 70s and the current temps would be more dramatic in the records.
Is that how it could be interpreted, if there was a AGW agenda involved?
Not saying there is one, but exploring the “what if.”
Since I live in Alaska (Valdez) and am about to move to Anchorage, I find these adjustments interesting. Also interesting to note is that the Matanuska site is in an area that became a bedroom community for Anchorage starting roughly in the late 1970’s and accelerating in the 1980’s and 1990’s. So any adjustment for Matanuska should be made to compensate for UHI there as well since it is much more built up than it was before the 1970’s and is definitely not as “rural” as it once was. As for the Anchorage site, it is a pretty fair distance from any UHI effect if it is out by the airport, since the coastal trail park area is out there and downtown Anchorage is several miles away. Much of the expansion of Anchorage’s population has been to the North (eagle river area), east (Muldoon area) and south (Huffman/O’Malley area). Temperatures in the Anchorage bowl can vary considerably between locations. In the winter of 2008/2009 for example, I was amazed driving around to find that locations as close as 1 mile apart could vary by as much as 10-12 degrees F.
Willis, I’m still waiting for you to answer my question. By all means, dance around all you want.
Meantime, another error in your piece: you refer to “(non) adjustment of the GISS temperature data for the Urban Heat Island effect” and immediately thereafter describe a procedure for adjusting urban stations based on the rural trend.
So once again: “falsus in unum, falsus in omnibus”?
I think I got whiplash trying to follow the graph of Matanuska’s homogenization. Can anyone recommend a personal injury lawyer who is more trustworthy than an IPCC scientist, or should I track down a GISS data specialist and have him apply his adjustments to my cervical vertebrae?
Given Matanuska’s sawtooth adjustment, should they adopt Katie Perry’s “You’re Hot and You’re Cold” as their theme song?
Hrm… That might make a cute video.
“The justification that is always given for these adjustments is that they must be right because the global average of the GISS adjusted dataset (roughly) matches the GHCN adjusted dataset, which (roughly) matches the CRU adjusted dataset.”
Not really surprising that when all three datasets are fudged the same way then come up with similar results.
To clear up the mystery of how Mantanuska gets adjusted – the Gistemp rules have just been changed.
See http://data.giss.nasa.gov/gistemp/updates/ :
January 16,2010:The urban adjustment, previously based on satellite-observed nightlight radiance in the contiguous United States and population in the rest of the world (Hansen et al., 2001), is now based on nightlight radiances everywhere, as described in an upcoming publication. The effect on the global temperature trend is small, that change reduces it by about 0.005 °C per century.
(page last modified 17 February 2010 23:13:44)
Mantanuska has a GHCN brightness index of 18, (C=bright, but under the new rules an index over 10 moves a station from rural to peri-urban or urban and so it gets adjusted)
See text_to_binary.f, dated 2010-02-18, in the updated sources to see the changes. (I’m basing this comment on an earlier version of this file, having spotted an archived trial run for this changed based on the 2009_10 Gistemp run, queried this with NASA, and received confirmation that this was a run to see the effect of this change). I have implemented this change already as an option in my own Gistemp implementation, and appended further details of the adjustment for Matanuska below.
This change, in so far as it relates to some areas I am familiar with, does drag at least some “rural” stations into the 20th/21st century as UHI adjustment candidates, but does also produce some amusing consequences – one of the adjusters of my “home” station, Dublin Airport, used to be Fort William (Scotland), now upgraded to peri-urban. The GHCN temperature record for Fort William however runs from 1884 to only 1903, a date possibly slightly earlier than the night brightness on which its classification is now based (as it happens, although an adjustment is calculated for Fort William, the net effect of this is no change).
The Gistemp (enhanced) log for adjustment, based on the December 2009 data, as I have not yet downloaded the January data, archived last week):
urb stnID:425702740010 # rur: 15 ranges: 1918 1990 500.
longest rur range: 1910-2004 91 [wgt: 0.523 238.4 km] 425702960000 [CORDOVA/MILE] UNITED STATES OF AMERICA
add stn 2 range: 1903-1990 87 [wgt: 0.152 424.0 km] 425701780000 [TANANA] UNITED STATES OF AMERICA
data added: 87 overlap: 77 years
add stn 3 range: 1919-2004 85 [wgt: 0.823 88.4 km] 425702510000 [TALKEETNA] UNITED STATES OF AMERICA
data added: 85 overlap: 85 years
add stn 4 range: 1933-2004 70 [wgt: 0.494 252.9 km] 425703410000 [HOMER/MUNICIP] UNITED STATES OF AMERICA
data added: 70 overlap: 70 years
add stn 5 range: 1942-2009 68 [wgt: 0.278 361.0 km] 425702310006 [MCGRATH] UNITED STATES OF AMERICA
data added: 68 overlap: 63 years
add stn 6 range: 1923-1990 67 [wgt: 0.532 234.2 km] 425702640020 [MCKINLEY PARK] UNITED STATES OF AMERICA
data added: 67 overlap: 67 years
add stn 7 range: 1943-2004 62 [wgt: 0.570 215.0 km] 425702710000 [GULKANA/INTL.] UNITED STATES OF AMERICA
data added: 62 overlap: 62 years
add stn 8 range: 1943-2004 61 [wgt: 0.174 412.9 km] 425702910010 [NORTHWAY FAA AP] UNITED STATES OF AMERICA
data added: 61 overlap: 61 years
add stn 9 range: 1921-1990 47 [wgt: 0.271 364.6 km] 425703400010 [ILIAMNA FAA AP] UNITED STATES OF AMERICA
data added: 47 overlap: 47 years
add stn 10 range: 1942-1990 46 [wgt: 0.626 186.9 km] 425702490000 [PUNTILLA] UNITED STATES OF AMERICA
data added: 46 overlap: 46 years
add stn 11 range: 1937-1970 31 [wgt: 0.350 324.8 km] 425702960010 [CAPE SAINT ELIAS ALASKA, U] UNITED STATES OF AMERICA
data added: 31 overlap: 31 years
add stn 12 range: 1944-1971 28 [wgt: 0.480 259.9 km] 425702490010 [FAREWELL FAA AP] UNITED STATES OF AMERICA
data added: 28 overlap: 28 years
add stn 13 range: 1949-1976 27 [wgt: 0.621 189.4 km] 425702640010 [SUMMIT/WSO AIRPORT] UNITED STATES OF AMERICA
data added: 27 overlap: 27 years
add stn 14 range: 1944-1966 23 [wgt: 0.054 472.9 km] 403719660010 [SNAG A,YT] CANADA
data added: 23 overlap: 23 years
add stn 15 range: 1949-1969 21 [wgt: 0.332 333.9 km] 425702600010 [NENANA/MUNICIPAL AIRPORT] UNITED STATES OF AMERICA
data added: 21 overlap: 21 years
possible range increase 32 69 72
“Also interesting to note is that the Matanuska site is in an area that became a bedroom community for Anchorage starting roughly in the late 1970’s and accelerating in the 1980’s and 1990’s. So any adjustment for Matanuska should be made to compensate for UHI there as well since it is much more built up than it was before the 1970’s and is definitely not as “rural” as it once was. As for the Anchorage site, it is a pretty fair distance from any UHI effect if it is out by the airport, since the coastal trail park area is out there and downtown Anchorage is several miles away. Much of the expansion of Anchorage’s population has been to the North (eagle river area), east (Muldoon area) and south (Huffman/O’Malley area). Temperatures in the Anchorage bowl can vary considerably between locations. In the winter of 2008/2009 for example, I was amazed driving around to find that locations as close as 1 mile apart could vary by as much as 10-12 degrees F.”
It’s almost as if getting accurate temp readings is more complicated than: adjustments to rural site data = j’accuse.
Mooloo (16:31:24) :
Robert:
Now the tricky question, Robert. How do you explain that this sort of oddity has appeared in GISS for Alaska, and also NIWA’s analysis for NZ, and in Australia too? Mere fluke?
Mooloo
As I suspect by your name you live in Waikato and you will probably be aware that Dr. .Jim Salinger’s doctoral thesis was the used as the basis for the NZ NIWA adjustments to the data which distort that record. The raw data shows no warming for NZ. Guess where Jim Salinger did his doctorate work? Yup you can guess. He gets several mentions in the infamous emails from UEA. So IMO that explains one leg of the ‘oddity’ Mooloo.
An Aussie will be able to provide the other leg maybe.
Doug
Don’t be too harsh on poor Robert, he builds beautiful little strawmen and then in obvious delight he burns them down with an amazing display of pyrotechnics. We should all stand in awe of his talents.
Robert (17:06:35)
I must have missed the question. What was it?
Get real. What I meant was that they claim to be adjusting for UHI but they are not actually adjusting for UHI. The adjustment in Anchorage is a good example. Claims to be for UHI, has nothing to do with UHI. Stop grasping at straws.
“Why should citizen scientists like myself have to dig out these oddities?”
My best guess? Because to a professional scientist who knows what they’re looking at, they’re not oddities.
If your accountant was calculating your taxes using the bewildering maze of poorly-documented corrections and mis-corrections that are becoming evident in this whole farce, and then told you they had lost all your receipts, would you pay what he said you owed?!
I would not pay a nickel in taxes based on such work! That is exactly my attitude toward Globaloney.
Actually its a (fairly) trivial job. You write a second program to analyse the adjustments and highlight those that are unexpected, such as adjusting rural station upwards.
When I see something like those adjustments, my summary for policymakers is: “Eeek!”
Question – can we compare global (or N Hemi) averages of raw data vs.
“adjusted data” to see the differences imposed by the cumulative adjustments? just curious.
thx
slg
ps – along the same lines, what ever happened to the comparisons between the ‘good’ US stations vs. the combined ‘good plus bad’ US stations as determined by SurfaceStations project? Did this show a significant difference so that we can get a handle on the overall impact of the siting issues (?)
Thanks.
Peter O’Neill,
Adjusting urbanization based on street lights is a potentially large oopsie. A single street light over snow would be as bright as six or more streetlights over grass or pavement.
Willis,
I have a theory. In the video a NOAA scientist explains that
the new electronic weather stations have a cold bias and
they add a delta to the data. So, if the per electronic stations
show higher temperatures, but you slide the delta increases
back to 1973, which includes the hotter pre electronic stations,
and then homogenize the whole series, would you not see
very rapid rise in temperatures?
Watch from 3:00 to 3:24
One thing is clear here, Robert is a troll. I will skim over his comments from now on. Little more than hyperbole.
As to Willis’s article, I am beginning to wonder if there are any trustworthy stations whatsoever, as even cursory review seems to reveal serial maladjustments. I would posit that it is either willful misconduct or endemic incompetence. Likely both. Nice work Willis.
I’d still love to see a Transactional temps record. By all means, munge the data, introduce adjustments, reverse them out fully or partly as new methods are invented, and substitute new adjustments.
But, leave the original reading as is.
And, every single measurement/adjustment/whatever, is a separate transaction for a station, on a day/time. And is categorised up the wazoo: type of adjustment code, who/what process put it there, etc.
Accounting has been transactional like this for, oh, a quarter of a century.
And SQL databases to handle large transaction volumes are common, cheap and reliable.
This reliance on a single data point per station per date/time is just so….amtuer hour.
Accounting for temperatures is what we need to bring the munging out into the daylight.
“What I meant was . . .”
There’s what you meant, and then there’s what you said. Are you starting to reconsider the wisdom of “falsus in unum, falsus in omnibus”? I would, if I were you. Never in the history of the English language has anyone started a persuasive argument with the words “What I meant was . . .”
I’d say its you who are grasping at straws.
The question was: what are the arguments in favor of the temperature record as reasonably accurate?
henry & Willis
US Census found Anchorage to have 153 per sq mile in 2000 vs 133 in 1900.
Climategate: The World’s Biggest Story, Everywhere but Here…
http://pajamasmedia.com/blog/climategate-the-worlds-biggest-story-everywhere-but-here/
When will the “old” MSM join the party ?
Picky, picky, picky.
Berniel is right. Billions and billions of scientists from all over the galaxy assure us that they have huge quantites of evidence that in a few weeks we’ll be scorched to death by AGW.
And you want to check the data!
Willis & henry
Re Matanuska population, does this help?
The 2008 population estimate for Matanuska-Susitna Borough, Alaska is 85,458.
Note also: Matanuska-Susitna Borough, Alaska
Population and Housing Narrative Profile: 2005-2007
Dave (16:56:57) :
Why is there even a practice to adjust temperature data at all? I’m a PhD scientist and work with raw data all the time. If urban heat islands and those sorts of things are having an impact on the data, then those factors should be used to describe the data during model building. You always run into danger when you “adjust” data here and there. Confusion arises over time: are you working with the raw data or adjusted data, and has it been adjusted properly. I for one always want the raw, unadulterated data to work with.
I’m with you Dave.
I could never understand why the raw data needed to be ‘messed about with’ but I’m not a PhD scientist. It seemed to me that distortions such as ‘Heat Islands’ could have been explained without altering data. I do understand however that the raw NZ data shows NO WARMING since 1853 but the adjusted figures do. So it is only because ‘scientists’ trained at UEA have rewritten the date that the climate has ‘warmed’. Go figure that one.
Doug
Seeme like the next task for Surface Stations is to expand to look at all statsions used in the record and to look at the temperature history of each station.
Given how critical golbal warmin is said to be you’d think someone would want to thoroughloy evaluate at least a significant proportion of the data and validate the “corrections”. That means there should be an extensive program of evaluating appropriate justifieable transparent and independent corection algorithms.
Now Surfacestations has been doing an excelent job with volunteers and very little funding. It isn’t as if there is any shortage of funding for climate research, i mean, if they can give David Barber $153million to go on a couple of cruises to look at ice I’m sure there must be funds to take a detailed look at the instrumental temperature data and create some real science experiments to realy understand what factors affect the data set.
Maybe take an urban area and the surrounding rural area and throughly grid them with temperature stations. A major city could have temperature stations every mile say.
This isn’t something volunteers should have to do it is something the scientists should have done at the very outset.
Those coordinates do come back to Palmer AK.
There is a listing for a Matanuska Agricultural Experiment Station. I think that’s what the “AES” stands for in that listing.
So Robert, no need to keep us in suspense any more, just provide your analysis of what the temperature adjustments are all about so that it can be considered. We have, as yet, nothing to work with from you.
Robert (17:43:04) :
“What I meant was . . .”
Robert, Willis didn’t think you understood what he had said.
He was just explaining it to you again. Starting the sentence out with
“What I meant was” is a way of communicating without sounding
condescending.
I however, enjoy your posts. They remind me of why I don’t believe.
I have a more extreme view: even if the temps went up as they say, its doesn’t mean anything in the realm of “climate”. They don’t even have to make it up as they obviously have…..
Robert (17:43:04) The question was: what are the arguments in favor of the temperature record as reasonably accurate?
Zero, none, you’re obviously not serious are you.
(When will the “old” MSM join the party ?)
When obamas PR team tells them too so don’t count on it anytime soon.
There are lies, damn lies, statistics, and warmist climatology.
My troll detector has gone off. See you tomorrow.
The Darwin post was v good, bowled me over. However not having the graphs of the before or after adjustment temps or anomolies for Anchorage or its neighbour made this post a bit flat, I thought. What did the pre-adjustment graph look like? What did the post -adjustment trend look like? Without that info, IMHO the adjustment graphs themselves look a bit like underwear flapping in the breeze. Much more interesting if you get a hint of what it was hiding…
‘robust’ as ever…
22 Feb: UK Financial Times: Public losing faith in science
By Clive Cookson in San Diego
Public trust in science as a whole has suffered from recent attacks on climate research, the head of the senior US scientific body admitted at the weekend.
“There is evidence that the corrosion in the public attitude to climate science has spread over to other areas of science,” said Ralph Cicerone, president of the National Academy of Sciences, citing public opinion surveys in the US and elsewhere.
Cicerone and other research leaders said scientists must work to regain public trust by being more open about their findings. “We need to be more transparent and provide more access to our research data,” he said…
But access requests need to be reasonable, Prof Cicerone said: “Some scientists are receiving requests bordering on harassment.”
Jerry North, a senior climate change scientist at Texas A&M University, agreed. “It seems that vilifying a scientist has become popular entertainment in the US,” he said.
Speakers at the AAAS conference said that neither the allegations of data suppression at UEA nor errors discovered in assessments by the UN Intergovernmental Panel on Climate Change had changed scientists’ minds about global warming.
“For many people who were not close to the science, questions arose about whether the robustness of the underlying science should be called into question,” said James McCarthy of Harvard University, who is chairman of the AAAS.
“Within the scientific community the answer is No,” he said. “If you took all the UEA data out of the package and removed the erroneous IPCC statements, it would not change the underlying science.”
Jane Lubchenco, head of the National Oceanic and Atmospheric Administration , the federal agency responsible for climate science, said the IPCC “had a wakeup call and is taking steps to address the mistakes that were made and to ensure that they don’t happen again.”…
Prof McCarthy was critical of the way the media had joined sceptics in attacking the idea of manmade climate change – as for example when they pointed to this winter’s heavy snow on the US East Coast as evidence that the world was not warming….
http://www.ft.com/cms/s/2/1700ab46-1dbc-11df-9e98-00144feab49a.html
Is the station at The University of Alaska Experimental Farm?
(History)
1917: Established as a United States Department of Agriculture (USDA) Agricultural Experiment Station
GISS data 1917 – 1990 Coincidence?
Forgive me if this has already been suggested, but how about compiling unadjusted data from a large number of rural stations and using that as a starting point? Forgive me, I’m a mere scientist working in a field where generating bogus data costs you your career.
Robert (17:43:04) :
“What I meant was . . .”
There’s what you meant, and then there’s what you said. Are you starting to reconsider the wisdom of “falsus in unum, falsus in omnibus”? I would, if I were you. Never in the history of the English language has anyone started a persuasive argument with the words “What I meant was . . .”
Robert. It occurs to me that you open your mouth and let the wind blow your tongue around. You really have nothing to say at all.
read all:
21 Feb: WSJ: Climate Change and Open Science In the Internet age, transparency is the foundation of trust
By L. GORDON CROVITZ
‘Unequivocal.” That’s quite a claim in this skeptical era, so it’s been enlightening to watch the unraveling of the absolute certainty of global warming caused by man. Now even authors of the 2007 United Nations report that “warming of the climate system is unequivocal” have backed off its key assumptions and dire warnings.
Science is having its Walter Cronkite moment. Back when news was delivered by just three television networks, Walter Cronkite could end his evening broadcast by declaring, “And that’s the way it is.” The Intergovernmental Panel on Climate Change (IPCC) report likewise purported to proclaim the final word, in 3,000 pages that now turn out to be less scientific truth than political cover for sweeping economic regulations…
Some in the scientific community are now trying to restore integrity to climate science. “The truth, and this is frustrating for policymakers, is that scientists’ ignorance of the climate system is enormous,” Mr. Christy wrote in the current issue of Nature. “There is still much messy, contentious, snail-paced and now, hopefully, transparent, work to do.” …
http://online.wsj.com/article/SB10001424052748704757904575077741687226602.html?mod=WSJ_Opinion_BelowLEFTSecond
Referring to :
======================
Peter O’Neill (17:12:34) :
To clear up the mystery of how Mantanuska gets adjusted – the Gistemp rules have just been changed.
See http://data.giss.nasa.gov/gistemp/updates/ :
January 16,2010:The urban adjustment, previously based on satellite-observed nightlight radiance in the contiguous United States and population in the rest of the world (Hansen et al., 2001), is now based on nightlight radiances everywhere, as described in an upcoming publication. The effect on the global temperature trend is small, that change reduces it by about 0.005 °C per century.
(page last modified 17 February 2010 23:13:44)
=========================
If the ‘effect’ is that small why bother with it at all? As I read it it is just a minor modification to an adjustment factor anyway. How much effort went into that analysis? It’s not necessarily a waste of effort – knowing that different approaches produce similar results can be of interest – as Willis pointed out in teh main article for a different context – but one has to wonder why someone elected to authorize the research and then implement a change that seems to be entirely inconsequential in the general scale of things.
Why add to the complexity and confusing of historic comparison and audit byt implementing such a trivial change? Why not just note that the analysis was done and leave it at that?
Unless, of course, for some reason – perhaps even something as innocuous as professional vanity or a name on a paper, the inclusion of the change mattered.
It might just make auditing more difficult in retrospect in, say, 20 years from now. Or it maybe opens up another useful but obscure variable for fudge factoring in the coming decades.
If there is another reason for making the change let’s hear it. at 0.005 °C per century the effort of the adjustment to the adjustment value is, at first site, not worth the candle.
[If I got my facts straight, 90% of the following is stipulated as fact by most of those here as just common sense science. I felt a need to spell it out, if just for myself…]
UHI is not the only thing the homogenization is supposed to deal with. Moves of met stations is also supposed to be covered in that. Another is the change in the time of day or whether the station’s methodology was to take the daily highs (regardless of times), or temps at fixed times – day and night (and what times those were, vs some other common/standard times). We all know that.
Moves are all documented (well, almost, anyway). With the numbers of stations out there, it seems difficult to understand why stations shouldn’t be selected SOLELY on the completeness of their histories. When they are made and documented, they do not keep occurring like the steps shown; ergo, those steps would seem to have nothing to – even partially – do with moves. Even if moves were made, they would show up as some extra ‘blip’ in the steps. In the absence of such blips, I am positing that no moves were made. (But then that begs the question, “Where is the UHI in all this?”)
Looking at the steps, one would first want to ask if there is any way Willis’ arriving at the steps was wrong – if he somehow misread the before and after data. If that was nailed down, then the adjustments need to be specifically tied to those reasons for adjustments. Whatever adjustments appear in the reconstruction, (which should be equal to the adjustments actually made), those adjustments need to be JUSTIFIED, data-wise. AND RECORDED – if not for others, then at the very least for the person DOING the adjusting, so he/she knows what he did at some earlier date.
Most of our data-focused questions really amount to this:
Everyone should be asking (very strongly) Mann et al to JUSTIFY why they adjusted in the direction they did, and in the amount they adjusted. FOR EACH STATION.
Of course, no one wants to really go there, poring over every station, do we? Jones has indicated as much. Mann just bull rushes every one, hoping to intimidate them with his juvenile tactics.
…In any before and after presentation of data, the timing and amount of any adjustments needs to have each step (literally, it seems) verified – i.e., JUSTIFIED. When it appears as artificial as what is shown above, of COURSE someone is going to ask, “Are you SURE about that, Professor Mann?” And, “Were the adjustments to various stations done en masse, or individually, and if en masse why did you choose the particular grouping that we find?”
If the above assessement is true, how anyone could justify the regularity of the steps in the Fairbanks data defies logic. If true, and if it is also true moves were certainly not made on a regular basis, then what do they represent?. Changes in the time or methodology of temp readings COULD not be made in such a manner, not without regularly shifting the temp reading times IN THE SAME DIRECTION (and there are only so many hours in the day, so the readings would begin to come back around on themselves). Are we to believe that they kept moving the met station incrementally out away from the city center, presciently far enough and regularly enough each time to keep ahead of the curve that would be part of a database decades later?
If true, the stepped appearance is clearly ONLY an artifact of the massaging done at GISS. Yet it is hard (nay, impossible) to conceive any reason for such an obviously artificial step progression for Fairbanks, by GISS or CRU or GHCN. Nature (especially meteorological nature) would not present such a linear trend. Why would they put anything so artificial into their adjustments? Laziness? Simplification? If so, the latter, they should have documented it. for later reference – and possibly improving it later.
…Vis a vis urban met station data, all adjustments to rural stations – especially ones used as the basis for nearby urban stations – should be doubly critiqued (and with the concomitant increased security of records with which to justify any such adjustments), since they affect not only their own readings, but those of other stations. That is only scientific common sense. Jumping Matanuska in the manner indicated is like pointing a laser pointer at this station and yelling, “LOOK! We just did anything we wanted to, and no one gave a damn enough to double check us!”
The justification of adjustments – this is what is being glossed over, even in the skeptical blogs, or at least not stressed and/or focused upon enough. Where are the records of the adjustments?
There was one bit of code (pointed out by Steve McIntyre I believe), in which the adjustment factor ins Mann’s (?) code was clearly stepped, in some cases removed for a time, then re-instituted. But the trend was highest in the 1990s. The code notes should have identified why those adjustments were made, with reference to some database somewhere.
Speaking of which: THERE SHOULD BE A DATABASE OF ADJUSTMENTS USED. It is clear that the CRU people did not adjust the same for every met station, that they chose some pattern of adjustments for each station and proxy that they thought was correct above all other adjutments schema for application to those particular stations/proxies. Homogenization requires each type of proxy to be adjusted differently (probably each batch, no less).
But homogenization per se should not be needed for thermometer readings. Adjustments are not necessarily the same thing as homogenization. Homogenization is to get the readings into the same SCALE. Thermometer readings are already ON the same scale. Thermometer adjustments are only of a shift-type – up or down – not of scale.
To be good science, the reasons for instrument adjustments need to be spelled out and documented as part of the methodology AND KEPT WITH THE PROGRAM OR DOCUMENTATION USED. If this is not done, calling these people “scientists” doesn’t equate them to those I’ve known and worked with. For work this sloppy, they would have been found out in very short order and canned.
In good science, EACH TYPE of adjustment also needs to be treated in isolation. As an example, a station might have had a UHI effect, PLUS a shift in location, and those two might be in the same direction or opposite direction. A “time of reading” change might take place at the same station, necessitating a third adjustment to the same raw data, and that could be either positive or negative, too.
Without such isolated adjustment factors being listed FOR EACH STATION, reconstructing by peer reviewers or replicating by other scientists would be like shooting gnats – it would be impossible to hit, because the amounts blend together. making it impossible to know what was being done.
For Mann/GISS or Jones/CRU to produce raw data without, it would be impossible for anyone to follow up and verify the work. And if the peer reviewers did not undertake to replicate at least SOME of the results, the peer review was worthless.
These two graphs (if correct) are strong evidence that the data was just arbitrarily pushed in certain directions. In Fairbanks’ case, the direction is in the cooling direction; in Matanuska it is both cooling and warming.
“WHY?” For both is the entirely correct question to be asked.
Mann needs to justify his steps.
He needs to justify why he was adjusting a rural station at all. When it is moving up and down like a yo-yo, how is comparison to it supposed to make any sense?
He needs to justify why he was adjusting Fairbanks when it was essentially a rural outpost.
And like Wills asks, “Do others have to re-do all of this from the ground up?” Arguments by Jones (as he makes in his recent Science interview) that others can just take the data (20% of which is not public still) and reconstruct it are garbage, and he knows it. If different adjustment figures are used, different results will come out of it all. And then it will be Jones/Mann et all vs the other studies, arguing back and forth as to who did what and – literally -when.
OT, but temperature ralated. Finally! Summer has arrived here in Sydney, Australia. Inner west, is as ~37c, being touted as a “scorcher”. 37c a scorcher for inner west, Sydney?? I don’t think so. 47c you might be getting there….but 37c is well within usuall variability for this time of year with the current prevailing winds (Westerly).
And then there are my friends in the UK who fly back to Sydney tonight who said to me last night that London Heathrow was closed due to snow. No reports of that here in the MSM. Can’t have stories of cold here, that does not fit well with KRudd747, Mzz W(r)ong, Mzz Gillard and Pete “How can we stop our beds burning” Garret.
“Since people are asking us to bet billions on this dataset,”
Trillions, Willis. Trillions.
Robert (17:43:04) :
I find your impetuous taunting of every contributor to this site rather comical really. You’ve become the Frenchman in the tower at WUWT:
[youtube=http://www.youtube.com/watch?v=9V7zbWNznbs&hl=en_US&fs=1&rel=0]
Re Robert..
I want someone to provide peer reviewed evidence that Robert has ANY scientific credentials, and that he is in an intelligent person. If such evidence is not provided, I will conclude that he has none and he is dense.
21 Feb: George F. Will: Global warming advocates ignore the boulders
Science, many scientists say, has been restored to her rightful throne because progressives have regained power. Progressives, say progressives, emulate the cool detachment of scientific discourse. So hear the calm, collected voice of a scientist lavishly honored by progressives, Rajendra Pachauri.
He is chairman of the United Nations’ Intergovernmental Panel on Climate Change (IPCC), which shared the 2007 version of the increasingly weird Nobel Peace Prize. Denouncing persons skeptical about the shrill certitudes of those who say global warming poses an imminent threat to the planet, he says:
“They are the same people who deny the link between smoking and cancer. They are people who say that asbestos is as good as talcum powder — and I hope they put it on their faces every day.”
Do not judge him as harshly as he speaks of others. Nothing prepared him for the unnerving horror of encountering disagreement. Global warming alarmists, long cosseted by echoing media, manifest an interesting incongruity — hysteria and name-calling accompanying serene assertions about the “settled science” of climate change. Were it settled, we would be spared the hyperbole that amounts to Ring Lardner’s “Shut up, he explained.”
The global warming industry, like Alexander in the famous children’s story, is having a terrible, horrible, no good, very bad day. Actually, a bad three months, which began Nov. 19 with the publication of e-mails indicating attempts by scientists to massage data and suppress dissent in order to strengthen “evidence” of global warming.
But there already supposedly was a broad, deep and unassailable consensus. Strange.
Last week, Todd Stern, America’s special envoy for climate change — yes, there is one; and people wonder where to begin cutting government — warned that those interested in “undermining action on climate change” will seize on “whatever tidbit they can find.” Tidbits like specious science, and the absence of warming?
It is tempting to say, only half in jest, that Stern’s portfolio violates the First Amendment, which forbids government from undertaking the establishment of religion. A religion is what the faith in catastrophic man-made global warming has become. It is now a tissue of assertions impervious to evidence, assertions that everything, including a historic blizzard, supposedly confirms and nothing, not even the absence of warming, can falsify.
http://www.washingtonpost.com/wp-dyn/content/article/2010/02/19/AR2010021903046.html
Re: George Turner (Feb 21 17:32),
As I said, NASA’s change “in so far as it relates to some areas I am familiar with, does drag at least some “rural” stations into the 20th/21st century as UHI adjustment candidates, but does also produce some amusing consequences”.
I may for example admit to some doubts about the newly acquired rural status of Baghdad and of Kabul Airport, but I will leave it to others more familiar with such areas to pass judgement. I’m just implementing Gistemp with enhancements to examine how it works, not writing the original code at NASA.
And apologies to the good citizens of Mantanuska for misspelling the name of their town at one point above.
News alert for WUWT. Sea level paper withdrawn from Nature Geoscience. I don’t know any other way to contact you guys.
http://www.guardian.co.uk/environment/2010/feb/21/sea-level-geoscience-retract-siddall
Two points:
1. Re Peter O’Neill’s post: “January 16,2010:The urban adjustment, previously based on satellite-observed nightlight radiance in the contiguous United States and population in the rest of the world (Hansen et al., 2001), is now based on nightlight radiances everywhere…”
Perhaps relying upon temperature devices located solely in North Korea will solve these snarky UHI adjustment problems. :~)
2. The best way to deal with “Robert” (who I’ve now seen trolling in two unrelated threads in the last two days) is to simply ignore him, here and everywhere. He’s obviously unworthy of anyone’s time or aggravation.
Peter O’Neill (17:12:34)
Peter, many thanks for that most recent information. Fascinating. However, I still don’t get it. How could satellites produce information useful for adjusting Matanuska downwards in say 1940? Or for adjusting it upwards in 1973? The fact that it is graded as “Urban” or “Suburban” today means nothing about what it was like in 1920. If it was still rural in 1920 (and there is absolutely no question that it was), why should the 1920 value be adjusted? This procedure makes no sense.
Gotta admire their balls, though. The scale of brightness goes from zero to 186, and anything over 18 is adjusted …
Also, their brightness numbers seem bogus to me. Care to guess the brightest site? New York? London? Cairo? Los Angeles?
No way. It’s Montreal … here’s everyone with a brightness over 150:
Philadelphia, 151
Riyadh, 153
Baltimore WSO City, 154
Sevilla/Tablada, 157
Valencia, 157
Atlantic City State Marina, 159
Jeddah, 167
Madrid/Retiro, 177
Edmonton Muni, 178
Shuwaikh, 181
Montreal Mcgill,Qu, 186
Shuwaikh is the second brightest temperature station on the planet? Really? If so, then brightness may not be a useful measurement.
At the other end of the scale, Srinagar is a city with a population just under a million, a population density of 6,400 people per square kilometre, and a brightness of 6, so it is rural. Baghdad has a brightness of 8, as does Jiuquan, China, population one million. Bangui is the capital of the Central African Republic, population half a million, brightness zero. Zero is also the score for Nanchang, China, population four million. … riiiiight. I looked up all of these on Google Earth, the temperature station is inside the city in all cases. And all of them will be used to homogenize other stations …
My sense is that we have the same problem here. Someone had a good idea (use brightness). But they didn’t detail an intern to actually look up each and every station to make sure that the brightness made sense.
Next, their weighting system is ridiculous. Examination of your posted data shows that the stations are weighted based, not on which nearby stations are closely correlated, and not on which stations are in the same climatological zone. They are weighted solely by distance … the formula for the weight is distance (km) / -500 +1.
I can think of no theoretical justification for that procedure. At least GHCN requires that their adjusting stations be correlated with the adjusted station, but GISS make no such requirement. And if you are going to weight by distance, wouldn’t correlation be likely to fall off by the square of the distance?
Finally, could you post a link to the “GIStemp log” you refer to above (or alteratively, post the stations used to adjust Anchorage)?
Many thanks for your outstanding contribution, science at its finest.
w.
Say what? What could possibly justify that kind of adjustment, seven tenths of a degree? The early part of the record is adjusted to show less warming. Then from 1973 to 1989, Matanuska is adjusted to warm at a feverish rate of 4.4 degrees per century … but Matanuska is a RURAL station.
Yes a rural station near a glacier as shown in the backdrop to the graph, definitely could have an effect.
John S (18:22:22) :
“Since people are asking us to bet billions on this dataset,”
Trillions, Willis. Trillions.
==========
Yep, you got that right.
Never mind that almost every county, state and most countries are bankrupt. The really scary part, is just how big a “trillion” is, and how fast they are spent.
Isn’t this just the same type of GISS adjustment we’ve seen in blink charts before? Adjust older temps down and newer temps up to create a larger trend. Looks like business as usual.
“Until they get off their dead … … armchairs and do the work they are paid to do…”
Can we call them crooks yet?
Still too early?
Let me know when we get to the prosecution stage of the global warming fiasco.
Robert and Willis
I appreciate both your perspectives, but not your tones. Science provides data, which can always be challenged. Both should be done politely, without accusation. As soon as the tone becomes strident, it becomes very difficult to change a position, and science becomes politics.
Anthony is more tolerant than I am. I would be inclined to snip all disrespectful text in articles, and comments. For example:
EdB (18:36:02) [snip … disrespectful]
That might bring the tone down enough to actually debate the science.
I’m pretty sure Peter O’Neill (17:12:34) is correct. The rural station got adjusted here because of the night lights.
So all Eschenbach needs to do is check the trends at the surrounding dark rural stations, because they’re the ones in the driver’s seat for long term trends. Which is what he should have done in the first place. Instead of “Not sure what I can say about that, except that I don’t understand it in the slightest. “, another couple hours of work would have provided the understanding he was seeking.
GP (18:18:01) :
That’s always a good question. If the change in method causes such a little change, why bother doing it? Well, it could be more important in certain regions. It may make little difference to the global numbers, but there could be individual grid points where the effect is stronger. This is actually often true of adjustments in general.
To everybody who’s annoyed that the GISS adjustments sometimes increase the warming trends in urban/town stations: The surrounding rural stations are the drivers. So whatever the trend is at the rural station, then GISS is going to impose that trend on the urban station.
Sometimes the rural station could truly be warming faster than the urban station. Just because a station is in a UHI doesn’t mean the UHI effect will increase over time. There could also be inhomogeneities in there that mess with the trend at either urban or rural station. A station move can easily obscure the true trend.
In addition the fudged sea level claims are being withdrawn: http://www.guardian.co.uk/environment/2010/feb/21/sea-level-geoscience-retract-siddall
Please disregard my previous comment as you posted the same thing as I was typing it.
What happens when you take away cloud cover and precipitation?
You get a warming effect and draught.
Just in certain regions of our planet.
But, winds can carry away this to other areas.
Is “Robert” Gavin’s screen name?
REPLY: No different person, he’s in the health care biz – A
The justification of adjustments – this is what is being glossed over, even in the skeptical blogs, or at least not stressed and/or focused upon enough. Where are the records of the adjustments?
Perusing through HARRY_READ_ME I got the impression that nobody really knows, as Harry was finding out that the programs were largely undocumented. How do you document a version adjustment when you are not aware that the program is adjusting or stomping data?
You should be aware, however, when looking at vintage 19th century data that 1880 can be juxtaposed or overwritten by 1980, data included. I’ve come across this in Solar data, so I suspect that some of the programs used in climate data have been imported from other efforts.
Not everything that happened to the data was intentional. Some of it was just plain unforseen. Never operate on the original copy of anything.
Willis Eschenbach (19:07:43) :
“How could satellites produce information useful for adjusting Matanuska downwards in say 1940? ”
They don’t. The GISS logic: if the station is currently urban or peri-urban, then it’s safest to not trust the trends at that station, over any time span. Let the surrounding rural stations take charge over the entire history of that station.
___
“The fact that it is graded as “Urban” or “Suburban” today means nothing about what it was like in 1920. If it was still rural in 1920 (and there is absolutely no question that it was), why should the 1920 value be adjusted? This procedure makes no sense. ”
But you know it went from being rural to urban or peri-urban at some point in time. So there’s a possible UHI trend in there somewhere in time, and you want to avoid it. If there is a station that is rural now, it probably always was. Let it do the driving.
That said, I prefer the NOAA thinking on this issue, over GISS’s approach. I look forward to GHCN v3.0. If you actually bother to compare the stations against each other, you can probably detect when the UHI trend took place. That’s what NOAA does.
___
“Bangui is the capital of the Central African Republic, population half a million, ”
This is an interesting point. Urban areas in poor parts of the world will lack nightlight. I wonder if they’ll let the GHCN ‘U’ rating override the nightlight data in such cases.
____
“I can think of no theoretical justification for that procedure. At least GHCN requires that their adjusting stations be correlated with the adjusted station, but GISS make no such requirement. And if you are going to weight by distance, wouldn’t correlation be likely to fall off by the square of the distance?”
The justification is in Hansen/Lebedeff (1987). Looking at the graphs of correlation vs distance, linear seems pretty fair.
carrot eater (19:17:26) : “To everybody who’s annoyed that the GISS adjustments sometimes increase the warming trends in urban/town stations: The surrounding rural stations are the drivers. So whatever the trend is at the rural station, then GISS is going to impose that trend on the urban station.”
I wish you could prove that statement. Or are you just a true AGW believer. If they are truly “the driver” then recent temps should be lowered not past temp, with the result of increasing the trend. And why get rid of rural reporting stations. For example, if rural stations are truly “the driver” as you claim how is it that the SF airport is the only station for northern California. And did they adjust for dwindling fog?
SteveGinIL (18:18:55) :
To be good science, the reasons for instrument adjustments need to be spelled out and documented as part of the methodology AND KEPT WITH THE PROGRAM OR DOCUMENTATION USED. If this is not done, calling these people “scientists” doesn’t equate them to those I’ve known and worked with. For work this sloppy, they would have been found out in very short order and canned.
Steve. How right you are. It didn’t happen here in N.Z. Instead we got a Phil Jones type response. No surprise really given Jim Salinger’s pedigree.
Here is how some of that ‘lack of good science’ played out in the House of Parliament in NZ on February 17th . last in question and answer time. It is an example of obfuscation by the Minister ‘protecting’ his department. Nobody should be fooled.
9. JOHN BOSCAWEN (ACT) to the Minister of Research, Science and Technology: Does the National Institute of Water and Atmospheric Research maintain an up-to-date schedule of adjustments of all changes made to the raw temperature data that are used in calculating the official series “Mean annual temperature over New Zealand, from 1853 to 2008”, published on the institute’s website; if not, why not?
Hon Dr WAYNE MAPP (Minister of Research, Science and Technology) : The “Mean annual temperature over New Zealand, from 1853 to 2008” analysis, which was referred to by the member, does make adjustments to the raw data from the seven stations. The reason that is necessary ………. The original methodology to do so was developed in Dr Salinger’s PhD thesis, which is also publicly available.
John Boscawen: Given that we have been through the information the Minister refers to and found no schedule of adjustments, can he point to where in this mass of information it is contained; if he cannot, can he commit to table in Parliament the simple schedule of adjustments?
Hon Dr WAYNE MAPP: The member is correct; there is a complex range of information on the institute’s website. The methodology for the site changes is published in the peer-reviewed International Journal of Climatology, ………….“Adjustment of temperature and rainfall measurements for site changes”. The huge volume of information indicates that this is quite a complex area.
Hon Rodney Hide: I raise a point of order, Mr. Speaker. With great respect, the Minister did not answer the question. The question is a very simple one. It is not about the methodology; it is about the simple schedule of adjustments. In his answer to the primary question he said that the schedule was on the institute’s web page, amongst this mass of information. We have been through it. There is no schedule of adjustments. So the Minister was asked whether he could point to the actual schedule of adjustments, not the methodology, and if he could not, whether he could table in the House the schedule of adjustments—not the methodology; the schedule.
Mr SPEAKER: I hear the honourable member, and I am sympathetic to the member’s point of order, because the question was on notice. The question asks specifically whether the institute maintains an up-to-date schedule of adjustments of all changes made to the raw temperature data. It does not ask why changes are made to raw temperature data, or how those changes are made; the primary question asks whether the institute maintains an up-to-date schedule of the adjustments made. That information should be available, and I believe that the House deserves an answer. I invite the Minister to give the House an answer.
He still only got a BS answer even after all that.
Doug
It seems to me that Robert doesn’t understand the difference between raw temperatures and adjusted temperatures. We are arguing that the real temperatures may be just fine, but the adjustments seem to make no sense, yet the public pronouncements about warming are based on adjusted temps.
Also, if the Matanuska temps are from the Palmer experimental farm, it is a long way from the built up part of the mat. valley. It is really rural and shouldn’t need any UHI adjustment unless the sensor is in the parking lot.
Jim Steele (19:36:55) :
“I wish you could prove that statement.”
Read and run the code, if you don’t believe me.
____
“If they are truly “the driver” then recent temps should be lowered not past temp, with the result of increasing the trend.”
The Anchorage trend was decreased, so you got what you wanted there.
As for what happened in Matanuska, you’ll have to examine the surrounding rural stations within 500 km. Hopefully Willis updates by putting those up; it is all that you need to close the puzzle.
Once Matanuska got classified as periurban due to the nightlights, then it lost driving privileges.
___
“And why get rid of rural reporting stations. For example, if rural stations are truly “the driver” as you claim how is it that the SF airport is the only station for northern California.”
Where do you get that? Looks like a ton of stations in Northern CA to me.
Lon Hocker
Following your logic,Anthony should have snipped Robert’s first comment,then the “tone” would not have been lowered.I thought Willis was rather restrained after being told
“No, you were dead on: you don’t understand what’s going on in the slightest”
Atlantic City State Marina, 159
#6 in the world?
David L. Hagen (17:43:11)
Per your citation it was 1990, not 1900.
Robert (17:43:04)
Y’know, Robert, you misconstrued my meaning entirely. I was trying to restate it in words of one syllable, so you might get it. I prefaced it by saying “what I meant was” to contrast it with your fantasy about what I meant.
As to your question, I fear I don’t have an answer. I don’t know any arguments that say that adjusting a perfectly good rural temperature record down and then back up by three quarters of a degree is a reasonable plan.
However, if you have them, bring them on.
David L. Hagen (17:54:40)
Unfortunately, that doesn’t really help. It is the population for the entire borough, not for the location where the temperature station is located. Good find, though.
3×2 (18:14:12)
Yes, the “AES” stands for “Agricultural Experimental Station”.
carrot eater (19:51:12) :
They don’t use the rural stations in N. Calif to compute the GIStemp, or whatever it is they call that 8-bit Atari Graphics map. Haven’t you been listening? The rest of the State could be buried under 100 feet of snow with howling subzero winds, but if SF is warm & sunny, the map will show a warm anomay.
More snow expected in Texas tomorrow & into Tuesday. Another round of cold slap for the NE.
Reno, NV broke a snowfall record today with 12 inches dumped.
Peter O’Neill (18:46:56)
Call me crazy, but I find it hard to laugh about dragging 1920 Matanuska data into the 21st century just so it can be adjusted …
Many thanks for your efforts. Could you post the stations used to adjust Anchorage? Much appreciated.
As someone who has spent a reasonable amount of time in Alaska, including the Matanuska valley, it’s “Matanuska” with one “n”, and the spelling error is from GISS/GHCN. Not surprising, as their station lists contains many such errors. QC ‘R US … not.
EdB (18:36:02) :
Re Robert..
I want someone to provide peer reviewed evidence that Robert has ANY scientific credentials, and that he is in an intelligent person. If such evidence is not provided, I will conclude that he has none and he is dense.
—————-
Of course, that proof of intelligence will require a 95% confidence level.
Phil. (19:10:30)
My apologies for the miscommunication. Although the first photo is of Anchorage, the second is not of Matanuska. My bad, should have made that clear.
@rbateman: hehe, I was talking to someone today whose sister lives up in the hills in the Reno area. She got 20″ of snow.
If I understand the above discussion Anchorage has been adjusted upwards at the rate of 1 degree per century of which therefore includes a -.005 C per century adjustment for UHI.
Matanuska on the other hand has been adjusted at the rate of 3 C per century downswards from 1910 to 1970 and then adjusted upwads at the rate of 7 C per century from 1970 to 1990 of which -.005 c per century was added because it is now considered affected by UHI.
So if there is in reality no UHI correction (-.005) what is the basis for these adjustments?
I think the state climatologists should be asked to answer these questions.
The following link compares Tucson and Grand Canyon adjusted unadjusted temps: http://www.climate-skeptic.com/2009/12/example-of-climate-work-that-needs-to-be-checked-and-replicated.html
A few points about GISSTEMP urban adjustment.
The nightlights approach. Nightlights is a really odd way to identify if a site is urban,rural or small town. The last time looked at it it was easy to spot obvious errors: Big cities by population that turned out dark and rural sites that turned out bright. This is because nightlights is a PROXY for urbanity.
The foundational studies on the correlation between nightlights and urbanity show this. When you have historical population data why do you think nightlights will be any better at assessing urbanity? Moreoever the real thing we are after is UHI. UHI is, according the theory, a positive bias that is created by changes to the physical ( geometry) and material changes in a site over time, and waste heat which is tied to population and human activity. Looking at the way GISSTEMP adjusts for UHI its quickly apparrent that in many cases the adjustments go against what theory says they should be. What’s missing in Hansen’s approach is some kind of verification that the adjustment actually works.
1. That the methodology for picking rural sites actually picks rural sites.
2. That the adjustments actually makes sense in individual cases
testing number one is a painful bottoms up proceedure. But it should not be so hard now that CheifIO has GISSTEMP running. Just output all the sites
that the program thinks are rural. Then go check each one.
Even more fundamental is the question of whether one can “adjust” for UHI at all. At best we have this. We have a number ( who knows how many) of sites which we could classify as Rural. Looking at the population, the nightlights, the vegatative index, the % of impervious surfaces, cannot replace LOOKING AT THE SITE. It would seem to me that one should start by picking the most pristine sites. That will give you a good picture. You may lack spatial coverage. Tough. that’s an uncertainity. The idea that you can take an urban site and “reconstruct” what it would look like if man was never there, is a HYPOTHESIS. The idea that you can regain spatial coverage by “adjusting” urban sites is a hypothesis.
Robert is just a FUD slinger. An errand boy sent by grocery clerks.
Farley State Marina in Atlantic City is next to Harrahs, Trump Marina, The Borgata, and Trump Taj Majal casinos.
They have lots and lots of exterior lighting to attract gamblers to the North side of town which is a mile and a half from the main roadway entrance to Atlantic City at the AC Expressway.
Though I would expect to see Las Vegas up there for the same reasons.
Perhaps the contrast line between the dark, cold Atlantic Ocean and the casino lights enhances the measurements.
henry (16:02:33) :
Matanuska-Susitna Borough population for:
1960’s: 5,000
1980: 17,816
1990: 39,683
2000: 59,322
2005: 75,001
2008: 85,458
Sources:
http://www.usgennet.org/usa/ak/state/boro-matsu.html
http://www.epodunk.com/cgi-bin/popInfo.php?locIndex=22230
http://quickfacts.census.gov/qfd/states/02/02170.html
Bear in mind that the borough covers 24,681.54 square miles.
I think you have the signs correct.
this is what I think they (the team) did.
1. cool the past for all that are trending level.
2. warm the past for all that trend warm.
3. level the ones that trend cooler.
net effect is a warming trend, and hard to prove that this is there end goal.
I.E. if they warmed the new readings today easy to show bias.
also by cooling the past you don’t need to mess with today’s readings !!!!!!!
Now prove me wrong with 5% or more that are reversed from the above 1-3.
Tim
Robert Kral:
“in a field where generating bogus data costs you your career.”
Unfortunately, in climate science, as in nuclear reactor regulation, scientific honesty would mean there are a lot fewer scientists and grants needed to study the resulting non-problems.
I once heard a talk by one of the aging pioneers in the nuclear field. He discussed how the difficult-to-measure effects of small doses of radiation were estimated by drawing a straight line back to the origin of the graph from the measureable effects of large doses. As the line gets closer and closer to zero the number gets tinier and tinier. But, multiplying the tiny number by a large population gives regulators numbers worry about. When he pointed out to these scientists that it’s not a straight line back to the origin, the curve has “hormesis” (it dips below the x-axis, small doses are actually beneficial), they would agree that research has consistently shown hormesis and say, “but, if we stipulate that, we’re out of a job as are most of the people in the regulation business.”
Same is true for a lot of pollutants.
Just as a warning, you have to be very careful with population density info in Anchorage as well. In Alaska, you have the state government, and then borough government. The Municipality of Anchorage is the equivalent of a borough, and while it is one of the smaller boroughs in Alaska, it is big enough to make any population densities worthless. See the link to the map below.
http://www.muni.org/Departments/Planning/PublishingImages/vicinity.gif
I have in-laws that live in the Anchorage Bowl, and I though it was a little more crowded than 162 people per square mile.
Food for thought. The comment at the link below includes a link to a paper about station moves in Anchorage. I also wrote a few comments about elevation and distance to open sea water in the Anchorage area and why that matters.
http://climateaudit.org/2007/04/11/some-china-comparisons/#comment-84788
By far, the biggest factor affecting air temperatures in Anchorage is the relative distance to Cook Inlet.
OTOH, the Matanuska station gets a lot of wind from the Matanuska and Knik glaciers. (Both of which have receded a whole bunch over the period of this discussion. Also, the Matanuska station is not really a great choice for supposed rural stability because the area around it has been growing faster than the rest of Alaska for 20-30 years.
If you want some better choices to check against potential UHI in Anchorage, see if you can find weather data for places like Skwentna, Nikiski, Kasilof, Big Lake, Knik, or Hope. Each of these has pretty minimal growth and are representative of weather conditions in the area.
The assessment of “rural” stations has been somewhat of a joke to those of us who live here and who have visited the sites.
The methodologies used to classify sites as rural or urban are not very clear.
One of my favorities is Peterson2003, that seminal study that shows no UHI effect. But when you look at the stations Perterson used in that study its pretty Goofy. Peterson also used nightlights, but looked like he used a different product. And he adjusted the time series with a TOBS program that was different from that developed by Karl. weird. Anyways here is a list of rural sites according to peterson for the US. Hint you wont find many of them in USHCN. BUT you do find some. lets just check a few. I’ll run down the list. Look at surfacestations and lets just see what our spot check shows. ok?
First we get a list of the sites:
http://climateaudit.org/2007/08/03/petersons-urban-sites/
Lets check a few rural sites: I’ll just go down the list of peterson sites
and pull the data if it exists in the photo database. It would be cool if the surface stations database had a pointer to all the data and all the charts, but
right now it doesnt. Some pages show the data and photos and others just show photos. Somebody should ask Anthony to see of this can be updated.
It would be cool if I could ask for a list of sites by population or by nightlights figure or by CRN rating or whatever..anyways
Here we go:
http://gallery.surfacestations.org/main.php?g2_itemId=24871
http://gallery.surfacestations.org/main.php?g2_itemId=57525
http://gallery.surfacestations.org/main.php?g2_itemId=16799
I had to pull data for this one.. see below
http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=425726080010&data_set=0&num_neighbors=1
More data for other peterson rural stations…
http://gallery.surfacestations.org/main.php?g2_itemId=36658
http://gallery.surfacestations.org/main.php?g2_itemId=37651
This NEXT ONE IS GOOD: it shows a station that peterson thinks IS RURAL
and GISS thinks is not rural
http://gallery.surfacestations.org/main.php?g2_itemId=3333
Pull the data
http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=425726940040&data_set=0&num_neighbors=1
HERE IS A NICE EXAMPLE: petersons NIGHLIGHTS method classifies this as rural
http://gallery.surfacestations.org/main.php?g2_itemId=12835
Data for that below:
http://gallery.surfacestations.org/main.php?g2_itemId=27187&g2_imageViewsIndex=1
Here is ANOTHER peterson Rural site? hmm nightlights dont see everything
http://gallery.surfacestations.org/main.php?g2_itemId=3421
a look at the temps
http://gallery.surfacestations.org/main.php?g2_itemId=6097
http://gallery.surfacestations.org/main.php?g2_itemId=20469
Rural by peterson and GISS
http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=425727930020&data_set=2&num_neighbors=1
The point of this little exercise is that the classification of sites into rural/not rural is a fundamental step in any analysis. with the right database and tools its not that hard of a job. i’ve also never seen a ‘tops down” approach to classifying that passed a simple spot check
does this make radiative physics wrong? no.
does this make climate sensitivity lower? i dunno
Can we do a better job of classifying sites if we progress bottoms up?
I think so.
Will the warming of the last century disappear completely? No.
Does it make sense to hand check these things? Yes.
Will people wave their arms and say “it doesnt matter?” Yes
Will people wave their arms and scream the data is corrupt? yes
Three kinds of lies: Lies, Damned Lies, and Climate Science
Three kinds of Liars: Liars, Outliers, and Out-and-Out Liars!
Thanks for another interesting post Willis. WRT Robert, the appropriate Latin is “nil carborundum illegitimi”.
Yes there is an Australian “leg”. Again this backs up what I discovered at Te Kowai- another Experimental Station!- except Te Kowai’s district population is given as 666.
I am desperately working to get a related post on my site re strange goings on at Gladstone, Queensland. Give us a day or so- i have to work too. Check kenskingdom.wordpress.com tomorrow (hopefully).
Ken
“Falsus in unum, falsus in omnibus”
Actually, the expression should rather be “falsus in uno, falsus in omnibus”. If you can’t get the Latin right, can you then be right about the rest? 😉
jcspe (22:11:40)
Excellent comment that exemplifies the real difficulties with the climate station records, the kind that can’t be fixed by some computer-generated adjustment.
I have friends in Wasilla and I know that’s true … so why would GISS adjust that time period by warming it??? (Having said that, the correlation between the Anchorage and Matanuska records is 0.9, very high …)
Here’s what’s in the GISS dataset
Skwentna only has data from 1948-1959
Nisiki no data
Kasilof no data
Big Lake no data
Knik no data
Hope no data
Welcome to climate science, where there’s never enough data, and what there is is sparse, short, and full of holes …
“Y’know, Robert, you misconstrued my meaning entirely.”
You made a mistake: get over it already.
“I was trying to restate it in words of one syllable, so you might get it.”
Since you’ve proven unable to grasp basic concepts like confidence intervals and external validity, I rather think our communication problems run the other way.
“I prefaced it by saying “what I meant was” to contrast it with your fantasy about what I meant.”
And I told you that “what you meant” is a lame excuse for the fact that what you said was completely wrong, as yourself admitted. End of story. Thanks for playing.
“As to your question, I fear I don’t have an answer.”
It’s not surprising. A skeptic, of course, would be able to consider the other side and the reasons he might be wrong. Somebody who operates on faith, of course, can’t imagine anything other than being right.
If you can’t open your mind enough to consider a hypothesis other than your own, then there in no point in discussing the subject. Not being a man of faith myself, I doubt we will convince each other.
steven mosher (20:47:59)
Mosh, excellent points all. Particularly, you say at the end:
Like you, I disagree entirely with the concept that you can “adjust” a station by comparing it with other surrounding rural stations. To understand why, let’s take a hypothetical example. Suppose we have a city station with UHI, surrounded by rural stations. And suppose all of the rural stations are in the identical climate zone, so much so that they show exactly the same temperature changes all the time. Finally, suppose that the city site is in the same climate zone, so that with no UHI would be just like one of the rural stations.
Now, in this situation we should be able to do a perfect “adjustment”. We can use the surrounding rural stations to perfectly remove all of the UHI from the city record, at which point it becomes exactly the same as the surrounding stations.
So now we can average all of them to get the true changes in temperature over time. But the key point is this:
We would get exactly the same result by just throwing away the UHI affected record.
The key point is that adjusting the records in this way does not add any new information to the information we already have. So even in a perfect world with perfect records, we cannot do any better than what we get by throwing the bad data out the window.
Now if we adjust the station records based on population, or based on known station moves, or based on known changes in the instrumentation, we have added information to what we had before.
But adjusting based on surrounding stations adds no new information. We can’t just take the same information to get ahead, that’s a perpetual motion machine. We’re better off to just throw away the UHI affected records, to get rid of the bad data entirely. Making the bad data look more like the other data still leaves some of the bad data in there. And in the real world, as we see in my opening post, in the quest to remove the bad data we often end up screwing up the good data as well.
Here’s a simpler example. Suppose we have measurements that we know should all be five. We get measurements like this:
5, 5, 5, 7, 5, 5, 5, 5, 5
We can use the good records (fives) to push the bad record (seven) in the right direction. But since the process is not perfect, we’ll end up with something like this:
5, 5, 5, 5.2, 5, 5, 5, 5, 5
As you can see, we’d be better off to just throw out the bad data.
So we can’t get ahead, and we’ll likely lose ground, all downside and no upside. I agree with you – if the data is corrupted by UHI and the rest, either fix it using new information, or throw it out. But don’t “adjust” it by comparing it with good data.
Finally, the whole idea of “homogenizing” the data is anathema to me. Nature is far, far from homogenous. Where I live it freezes maybe once or twice a year, some years not at all. A quarter mile away, it freezes ten or twelve times each and every year.
Would we gain anything by averaging these, by homogenizing them? I say no, doing that loses data rather than gains data. Nature is patchy and blotchy. Microclimates are the rule rather than the exception. Any attempt to homogenize nature merely makes nature’s blacks and whites into a mushy dull gray … where is the gain in that?
Thanks for your contributions, Steve, always welcome.
w.
Willis Eschenbach (15:37:55) :
Since the early years of the Anchorage record are adjusted to be warmer than the later years, this reduces the apparent warming.
Doesn’t that irk you in itself. It does me.
Why would NASA’s GISS division raise the temperatures in 1926 when there were no influences of UHI instead of lowering today’s temperature in a city to adjust for UHI.
In itself, that keeps the temperatures today jacked up as if the whole rural world were a huge city and the whole world has heat-island influences everywhere but in the cities. Seems something is very wrong.
Willis…PLEASE quit responding to Robert as it is a COMPLETE waste of
time.
denny (23:44:46)
Yeah, denny, you’re probably right. But I don’t want someone to read the thread and say “Willis didn’t answer questions from someone who was opposed to his ideas,” particularly since Robert made that exact accusation.
This way, people can see that I do try to answer all questions, not just the easy ones, and that everyone gets a fair hearing, including and particularly people who disagree with what I say.
Next, although I might never get Robert to see the light, there are many more lurkers than people who post. I want to expose to them the shallowness of the arguments of many of the AGW crowd, and Robert is a paragon exemplar of that. He constructs straw men at a rate of knots, raises meaningless objections to things I never said, tosses out gratuitous insults, and avoids the issues as though they had fangs. What better example could I want? If he didn’t exist, I’d have to invent him …
Finally, the more he argues, the more opportunities I have. For example, Robert brings up Tamino as though he were a scientist. That gives me a chance to discuss the issue of the rampant censorship on Tamino’s “scientific” blog (laughably called “Open Mind”), and to invite Grant Foster to stop being an anonymouse and debate the issues in public where he can’t squash scientific disagreement, and to ask Robert if he thinks censorship and science are compatible.
Robert may or may not provide an answer, but it gives lurkers and posters alike the chance to ponder the issues and answer the questions for themselves. And that to me is a worthwhile thing.
Is this the right place to look up the brightness index?
http://data.giss.nasa.gov/gistemp/station_data/station_list.txt
Re: Willis Eschenbach (Feb 21 19:07),
Anchorage:
urb stnID:425702730000 # rur: 15 ranges: 1916 2009 500.
longest rur range: 1910-2004 91 [wgt: 0.488 256.0 km] 425702960000 [CORDOVA/MILE] UNITED STATES OF AMERICA
add stn 2 range: 1903-1990 87 [wgt: 0.086 457.3 km] 425701780000 [TANANA] UNITED STATES OF AMERICA
data added: 87 overlap: 77 years
add stn 3 range: 1919-2004 85 [wgt: 0.755 122.5 km] 425702510000 [TALKEETNA] UNITED STATES OF AMERICA
data added: 85 overlap: 85 years
add stn 4 range: 1918-2009 84 [wgt: 0.079 460.7 km] 425703260000 [KING SALMON] UNITED STATES OF AMERICA
data added: 84 overlap: 79 years
add stn 5 range: 1933-2004 70 [wgt: 0.608 196.2 km] 425703410000 [HOMER/MUNICIP] UNITED STATES OF AMERICA
data added: 70 overlap: 70 years
add stn 6 range: 1942-2009 68 [wgt: 0.293 353.5 km] 425702310006 [MCGRATH] UNITED STATES OF AMERICA
data added: 68 overlap: 68 years
add stn 7 range: 1923-1990 67 [wgt: 0.434 282.9 km] 425702640020 [MCKINLEY PARK] UNITED STATES OF AMERICA
data added: 67 overlap: 67 years
add stn 8 range: 1943-2004 62 [wgt: 0.466 266.9 km] 425702710000 [GULKANA/INTL.] UNITED STATES OF AMERICA
data added: 62 overlap: 62 years
add stn 9 range: 1943-2004 61 [wgt: 0.067 466.4 km] 425702910010 [NORTHWAY FAA AP] UNITED STATES OF AMERICA
data added: 61 overlap: 61 years
add stn 10 range: 1921-1990 47 [wgt: 0.380 310.3 km] 425703400010 [ILIAMNA FAA AP] UNITED STATES OF AMERICA
data added: 47 overlap: 47 years
add stn 11 range: 1942-1990 46 [wgt: 0.651 174.3 km] 425702490000 [PUNTILLA] UNITED STATES OF AMERICA
data added: 46 overlap: 46 years
add stn 12 range: 1937-1970 31 [wgt: 0.332 334.2 km] 425702960010 [CAPE SAINT ELIAS ALASKA, U] UNITED STATES OF AMERICA
data added: 31 overlap: 31 years
add stn 13 range: 1944-1971 28 [wgt: 0.499 250.6 km] 425702490010 [FAREWELL FAA AP] UNITED STATES OF AMERICA
data added: 28 overlap: 28 years
add stn 14 range: 1949-1976 27 [wgt: 0.524 238.3 km] 425702640010 [SUMMIT/WSO AIRPORT] UNITED STATES OF AMERICA
data added: 27 overlap: 27 years
add stn 15 range: 1949-1969 21 [wgt: 0.238 381.0 km] 425702600010 [NENANA/MUNICIPAL AIRPORT] UNITED STATES OF AMERICA
data added: 21 overlap: 21 years
possible range increase 42 85 86
E O’Connor (00:47:11) : edit
Yes. Also see the other list here for further metadata.
Willis,
Can you confirm something for me?
I see the GISS homogenization adjustments for Anchorage in fig #1 as follows:
1) In the mid 1920s GISS added .9C to the raw temperature data to get their homogenized adjusted temp data
2) around year 2000 GISS added 0.0C to the raw temperature data to get their homogenized adjusted temp data
3) in the ~85 years between GISS linearly (in stepwise fashion) decreased what the degrees C that they added to the raw temperature data to get their homogenized adjusted temp data
4) From late 1940s to present there was a significantly amount of urbanization as evidenced by the population curve.
Do I have the items 1), 2) and 3) above right?
Is so then I have some further observations to make, but first making sure I got the above right.
John
Willis Eschenbach (00:35:49) :
‘Robert may or may not provide an answer, but it gives lurkers and posters alike the chance to ponder the issues and answer the questions for themselves. And that to me is a worthwhile thing.’
Willis. The patience of Job and the wisdom of Solomon make you a rear beast indeed. Chuck in your dry any pithy humour and you might have the makings of a ‘universal man’!
But compliments aside, your comments have been enlightening for me and for that many thanks
Regards
Doug
I think I’ve figured out what is going on here. The relevant parts of the code are in directory STEP2 of the GISTEMP source. The first thing to note is the file v2.inv in ./input_files. It lists the station data, and for the two Alaska stations:
42570273000 ANCHORAGE/INT__________________ 61.17 -150.02__ 40____8U__173FLxxCO 1A 5WATER__________ C__ 53
42570274001 MANTANUSKA AES__________________61.57 -149.27__ 46__225R__ -9FLxxCO30x-9TUNDRA__________C__ 18
The second last number gives the GHCN brightness rating – C is highest. So both will be adjusted.
The adjustment is done in the subroutine adj() of padjust.f. Temperatures are held as integers, to tenths of a degree – 121 is 12.1C. The relevant code fragment is:
____ do iy=iy1,iy2
________sl=sl1
________if(iy.gt.knee) sl=sl2
________iya=iy
________if(iy.lt.iy1a) iya=iy1a
________if(iy.gt.iy2a) iya=iy2a
________iadj=nint( (iya-knee)*sl-(iy2a-knee)*sl2 )
iy is the year. You’ll see that there is provision for a “knee” and two slopes (switching at the knee). There are also limits beyond which the adjustment will be held stationary.
So the effect of the “nint” is that the adjustment is piecewise linear, and forced to the nearest integer value – ie 0.1C.
So now you can see where those plots come from. Anchorage has no knee, but just a steady slope adjustment, made stepwise by the nint. Mantanuska has a knee, and a steep slope following the knee.
The rationale seems to be that a broad slope correction is made to match the city to have the trend of its rural surrounds. The knee is presumably to allow for a transition in the past from rural to urban.
Sould be rare beast! sorry Willis!
The common factor in the errors is the expectation of those manipulating the Data: that means if the expectations are wrong the conclusions are wrong.
True scientists alter the theory to fit the facts not the facts to fit the theory.
‘ Steinar Midtskogen (22:36:00) :
“Falsus in unum, falsus in omnibus”
Actually, the expression should rather be “falsus in uno, falsus in omnibus”. If you can’t get the Latin right, can you then be right about the rest? ;)’
Yes, you can. As in “coito, ergo sum”.
Raw data is good for you and may be digested later as food for thought.
I was in the military and spent a large proportion of my sdervice in norfolk(uk) home of the uea. We used to call the locals carrot crunchers. So my question is, carrot eater, is that you philj or briffa
?
John Whitman (01:39:04)
John, I don’t understand how or why they constructed the stepwise change in temperature. Your explanation seems as good as any. However, why on earth would they want to change the mid 1920s temperature by adding almost a full degree to it?
In any case, make your observations, always welcome …
Re: Nick Stokes (Feb 22 02:00),
More on the algorithm used. In PApars.f they compute, as they stated, for each urban station (in the do 200 loop) the AVG() of yearly distance-weighted average values of nearby rural stations. They then calculate the difference between that and the urban values URB(). This information is passed to getfit() in t2fit.f (via the common block FITCOM).
In getfit(), the lines-with-knee fit to AVG-URB is laboriously computed by trying every possible knee (within 5 yrs of the end of range), and choosing the value with least residual SS (in trend2(), tr2.f). The resulting slopes and knee are what ends up in the adj() routine set out in my previous post, and with the steppedness of integer conversion become the plots shown in the head post here.
Willis Eschenbach (23:19:45) :
That’s the whole point of the GISS adjustment. The stations labeled as urban do not get to impact the long term trends in the final result. They’re left in there to contribute some short-term wiggles to the local averages.
So yes, they’re essentially tossing out the urban stations. If you don’t like that, then follow the GHCN adjustment set instead. And if you don’t like adjustments at all, you might note that the unadjusted data give about the same global mean trend, anyway.
As for your continual wonderment at what happened: just go and see the neighboring dark rural stations. Then apply the two-legged adjustment described in the papers, if you really want to recreate the math. Do you want to simply wonder, or do you want to learn the answers? There is nothing stopping you from the latter.
Willis Eschenbach (23:19:45) :
Incidentally, you’re using the word ‘homogenize’ incorrectly in this context. In this field, inhomogeneities refer to the things that mess with the record at any given station: station moves, instrument changes, etc.
Nick Stokes (03:15:17) :
The ‘knee’ used to be at a fixed point in time, I think 1950. Allowing it to float makes for a better calculation, but adds to the computation as you can see.
Rural it is then. Don’t quite understand how it scores 18. Moonlight reflected in they eyes of Wolves perhaps?
Wouldn’t it be great if the warmists and the sceptics could agree a number of rural and always-been-rural sites, and look at the raw data without any fiddle factors, and see just how much warming there really is…
Why elevate the erly temperatures for an urban site?
Well, the net effect is to reduce the apparent UHI effect.
That means that later uban data can be adopted in the later years with a lesser correction meaning that the average leter years temperatures is elevated.
This isn’t about reducing he apparent warming on this site but getting the later temperatures into the data set with no signinificant reduction – i.e. don’t pull down the later temperatures for the expanded city, bring up the earlier temeratures.
This works if the earlierr temperatures do not significantly increase the average global temp but the later urban dat, if included with minimal correction, does significantly increase the mean global temp.
AT least, that’s one possibility. unless I have misinterpretted this somwehere.
There are 29 rural (by light at night) stations in Alaska within 1000km of Anchorage. Here they are listed by increasing distance in km.
DistanceStation
109SKWENTNA
126TALKEETNA
177PUNTILLA
245SUMMIT/WSO AIRPORT
252FAREWELL FAA AP
256CORDOVA/MILE
265GULKANA/INTL.
281MIDDLETON ISLAND/AUTO
289MCKINLEY PARK
311ILIAMNA FAA AP
323MINCHUMINA
334CAPE SAINT ELIAS ALASKA, U
427YAKATAGA/AIRPORT
457TANANA
466NORTHWAY FAA AP
508ANIAK/AIRPORT
526HOLY CROSS
598YAKUTAT
600EAGLE
614ALLAKAKET
629UNALAKLEET
643BETTLES
648FT YUKON UNITED
722MOSES POINT
855GUSTAVUS/2 SW
880KOTZEBUE, RAL
917UMIAT
944ANNEX CREEK
994COLD BAY
It appears that the Weather Underground mean temperature for January in Anchorage is a full 1 degree plus C cooler than the GISS reading (16 F or -8.9 C, vs. -7.4 C). Given that 2009 readings at both places were similar, this seems a little strange.
I’ve also noticed this in the Vladivostok readings, which registered a full 3 degree C difference (-12.3 C vs. 4 F or -15.6 C)
It appears that this difference has existed for some time (different locations?), but that the gap between readings has been increasing over the past decade:
WU GISS
1997: 5 F (-15C); -13.7 C
1998: 4 (-15.6); -14.3
1999: 11 (-11.7); -10.3
2000: 3 (-16.1); -15.1
2001: 1 (-17.22); -16.2
2002: 12 (-11.1); -9.4
2003: 7 (-13.9); -12.1
2004: 7 (-13.9); -11.2
2005: 7 (-13.9); -11.8
2006: 6 (-14.4); -12.6
2007: 15 (-9.4); -7.1
2008: 1 (-17.2); –12.8
2009: 10 (12.2); – 10.8
2010: 4 (-15.6); -12.3
What gives?
Nick Stokes (05:31:29) :
I think they only use those within 500 km if there are enough stations within that range, which appears to be the case.
So now that we have a list of the stations we need to look at, to answer all of the questions Eschenbach raises…
Thank you Willis for providing that extra GISS link on index brightness.
It was interesting to look at the entries for my country, Australia, and other countries which interest me such as Estonia, Latvia and Lithuania.
I have some nitpiky comments.
The information on which the Brightness Index is calculated could be out of date. Hobart Australia has an index of 34 and Canberra an index of 30. Canberra’s population has overtaken Hobart’s by at least 100,000. They both however end up as C in the metadata so it doesn’t matter.
There is a similar disparity in the index for Vilnius and Kaunas (10 and 17) in Lithuania.
To find the cities for Estonia, knowledge of Baltic, Hanseatic and some modern history helps.
Tallinn: The capital and the second n is dropped in both sets of data.
Baltischport :This is the Russian name for the town of Paldisksi about 50 km west of Tallinn. It is marked as USSR.
Pjarnu: This seems to be the Russian spelling for Parnu.
Dorpat: This is the German name for Tartu, Estonia’s second largest city. It is also stated as being in the USSR.
Fellin: This is the German name for Viljandi. Again another USSR listing.
The Latvian data has the same oddities of German names and USSR locations.
Sure the co-ordinates (which I didn’t check for accuracy) are the critical elements for location and these are just nitpiks. But it is a tad sloppy.
pat (18:44:46)
AGW has been declared a religion by a UK court in 2009. An employee took his company to court for preventing him or sacking him because of his religious belief i AGW. Fact
Veronica (England) (05:20:11) :
This was done by a north american school boy last year and somewhere on the climate realist sites there is a video of his explanations and chart. Try a google search or perhaps one of the readers here can remember where it was.
Can anybody (or hopefully somebody) provide the world, or at least me, with the confidence that:
1) all these immense efforts and all this time and money spent by good as well as bad scientists on collecting and interpreting (or deliberately misinterpreting) hundreds of thousands of temperature data can ever lead to a believed average world temperature graph?
2) that there is any use in attempting to do this, BEFORE it has been established with certainty that (a possibly dangerous) upward or downward trend is caused by human activity rather than natural forces?
3) if neither, that the efforts mentioned under 1) are still worthwhile?
Thinking outside the box …
Steve Mosher advises that: “UHI is, according the theory, a positive bias that is created by changes to the physical (geometry) and material changes in a site over time, and waste heat which is tied to population and human activity.”
Since human activity produces UHI warming, and in light of the enormous growth in global population centers resulting from industrialization, perhaps the IPCC religionists have been right all along about AGW … but for the wrong reason.
Wanted to ask this again……along the lines of what the Carrot Eater mentioned above…
Question – can we compare global (or N Hemi) averages of raw data vs.
“adjusted data” to see the differences imposed by the cumulative adjustments? just curious.
Also along the same lines, what ever happened to the comparisons between the ‘good’ US stations vs. the combined ‘good plus bad’ US stations as determined by SurfaceStations project? Did this show a significant difference so that we can get a handle on the overall impact of the siting issues (?)
Thanks
Willis,
Here’s an odd thought. Can you compare the difference rural vs. urban temperatures – broken into weekends and holidays versus workdays? On top of that, how about throughout the calendar year?
I expect that there will be a difference based on absolute temperature, as heat pumps kick on one way or the other, etc, on top of whether cars are showing up in the parking lot.
I would argue that there is no single-value adjustment that can compensate or because the UHI is a variable effect due to the inconstancy of human activity. This should leave fingerprints in the data.
S. Geiger (08:15:52):
I don’t know the answer to that. But I do know that the large drop-off in temp stations has had an effect on the GISS record: click
Chart by Steve Keohane, who made, IIRC, it by subtracting this from the GISS data: click
@Alan S (15:55:04) :
“I have extreme difficulty understanding, from GISS policy as related above, why any rural station would be adjusted upwards. I assume I am missing something obvious and would dearly like to be enlightened.”
I had trouble with that, too, until I realized that they are doing things a bit backwards. They are assuming that the highest urban readings, which include UHI, are the correct temperature. Therefore the rural temperatures need to have UHI added in. It’s a very peculiar way to do things. One would think that you should subtract the UHI out. Maybe they just like high numbers!
Idwen is the old German name of Idus, some 7 km from Rūjiena, northern Latvia. I sincerely doubt there ever were any observations made in that God-forgotten place, so this name may refer to Rūjiena itself. It’s a ve-e-ery small town (population ~3.5k) and might well qualify as ‘rural’ though.
Mitau is the old name of Jelgava, which is listed as ‘rural’ despite having population above 60k.
Three locations in Spain and three in Saudi Arabia/Kuwait on the list, but none in Japan? Something seems to be screwed badly, a quick look at this map tells me.
At the risk of feeding the Troll…..
How can you instantly recognise an AGW fantasist? Somewhere along the line of their smug, patronising mantra driven “argument” (that of the non-scientific, non-mathematical variety), they cannot resist using the words “tinfoil” and “hat”. It is programmed into them at an early stage.
Example:
*******************************************************************************
If you want your guesses to be taken seriously, you need to make a little more of an effort to figure out what is going on before you go all tinfoil-hat on the subject.
*******************************************************************************
And then they confuse something fundamental (like who needs to justify what, to whom)
Example:
*********************************************************************************
I’ll ask you again: can you accurately describe any of the arguments in favor of the temperature measurements as they are?
*********************************************************************************
No – it is the AGW fantasists that need to do that, actually.
And then they get breathtakingly ironic (without meaning to be)
Example:
*********************************************************************************
If you can’t imagine how you could possibly be wrong, or understand the efforts that have been made to insure the data’s accuracy, there’s no point in challenging your faith with facts.
*********************************************************************************
I laughed until my ribs hurt quite a lot, when I read that gem.
I wonder if the Troll would be so insufferably rude, if he had to face those whom he insults, rather than hide behind a keyboard, somewhere on the internet?
Matanuska station has not just sat in the same place since its inception. Its moved around a bit.
http://climate.gi.alaska.edu/history/CookInlet/Matanuska.html#History
Not to mention the fact that in 1966 they changed the time of observation.
Robert is obviously a troll,
He gives nothing but negative feedback….Perhaps the computer modelers should have talked to him before they added all that positive feedback
It is always best to ignore trolls. If he knew anything at all about the science discussed he would have presented it.
I have the basic temp-humidity- pressure system (three seperate 24 hr recording instruments) on my rural property 25 miles from an official station Modesto Ca. Airport. When I check my recorded data it runs typically 2/3C cooler than the official temp.
Is there any move to set up a legitimate NGO system to counter the manipulation the governent stations are obvioulsy doing?
A less generous interpretation is, it shows that they’ve been coordinating their lies.
Yes, everything has to start over from the raw data, if even that can be trusted.
carrot eater (19:17:26) : edit
I still don’t have the understanding I’m seeking. Perhaps you could tell us why temperatures in Matanuska in e.g. 1930 should be adjusted at all? I don’t mean “because it’s classified as an urban station”. Even if it is urban now, it wasn’t in 1930, so your explanation is no reason to adjust the 1930 data as GISS has done.
I mean, what is the theoretical justification for adjusting the temperature in 1930? There is (AFAIK) no evidence that the thermometers were wrong, or anything like that. So carrot eater, since you want to rag on me for my lack of understanding, please enlighten us. Why adjust perfectly good data?
w.
“No – it is the AGW fantasists that need to do that, actually.”
You’re wrong, Willis. You’re just wrong. Science is not a sport in which people tally up all the evidence for what they want to believe, and leave the identification of contrary evidence and alternative explanations to people who want to believe those.
If you want to be taken seriously as a self-identified “skeptic,” you need to consider all sides of the issue, and be open to the idea that you might be wrong.
I’m not going to participate in your Crusade, not even by opposing you. If I hear one tiny bit of genuine openness to contrary evidence, or real curiosity about the world that extends beyond advancing your case, you will find me the most eager of correspondents.
Until then, I don’t think there’s anything left to say.
carrot eater (19:17:26) : edit
I would agree with you, if the algorithm actually produced reasonable results in the trends imposed on the data.
But given that Matanuska and Anchorage are only 30 miles apart, what is the explanation for the huge difference in the two adjustments? Matanuska uses every rural station for adjustments that Anchorage uses except King Salmon, yet their adjustments are totally different. To me, that spells “bad algorithm, no cookies”.
Yes, the surrounding rural stations are the drivers … but in the event, they’re driving the adjustments off the cliff. Merely saying, as you do, that it is the fault of the rural stations is assuredly correct … but it doesn’t solve the underlying problem.
Just checking: have you done a similar analysis of other stations that did not show such strange findings (beyond the Australia and Alaska stations)? Just wondering about publication bias.
carrot eater (19:36:51)
And you agree with that?
So for decade after decade, a station’s data is so trustworthy that it is used to adjust the data of certain surrounding stations. It’s one of the rural stations that “take charge” of other stations and modifies their data.
But then, after decades of being the gold standard, because in 2010 a station finally exceeds a certain number of lights around it, the 1930 station data is suddenly not trustworthy??? Suddenly, after decades, it’s “safest” not to trust the 1930 data??? That makes sense to you?
I understand that this is the GISS claim … I just find it a totally nonsensical claim. I was hoping that there might be a reason that was, you know … reasonable.
S. Geiger (08:15:52) :
To compare raw and adjusted temperatures in the entire global dataset, for GHCN (not GISS), see under Q4 here.
http://www.ncdc.noaa.gov/cmb-faq/temperature-monitoring.html
Not much to look at.
That’s global. In the US alone, adjustments make more of a difference; among other places see some figures and discussion here (both NOAA and GISS, this time)
http://pubs.giss.nasa.gov/abstracts/2001/Hansen_etal.html
Well, Willis, I’ll stand by my pseudo-scientific reason that past stations need adjustment. The 1930’s stations already received their sunlight input back in the 1930’s, but according to the laws of pseudo-thermodynamics (entropy) their temperatures will continue to decay. GISS, agreeing that the laws of physics should apply just as much to the distant past as the present or future, take this into account. I’ve looked at the global trends in 1930’s temperatures and have verified that the decade is slowly sliding into an ice age, just as pseudo-entropy theory would suggest.
See, it’s all perfectly reasonable, in a pseudo-scientific kind of way! 🙂
carrot eater (03:38:53) : edit
You better write GISS and tell them of their error, since at their site they refer to the final dataset that I used as “after homogeneity adjustment”. So what I have graphed is by definition the “homogeneity adjustment”.
Willis Eschenbach
First, you say the surrounding rural stations are driving it off a cliff. You can’t just say that. You have to open up all the surrounding rural stations, see how well they correlate with each other and Anchorage and Matanuska, and then combine all those rural stations and see what the average trend is. Without doing this work, this analysis here will remain less than half-done, and uninstructive. Perhaps it will turn out that it’s clear that Anchorage and Matanuska should have whatever trends were imposed on them. Or perhaps the whole thing is a spurious result. But until you do that work, you cannot know.
and Anthony and WUWT just got another mention in this Fox News story
Willis Eschenbach:
As for a station going from rural to urban: I’d have thought you’d be happy with GISS. They are taking the more conservative route, and the one most aggressive in removing UHI.
Take Matanuska. You know it was probably nice and rural a long time ago. Now there are some nightlights; just enough to make you wonder. Before looking at the temperature record, you have no idea when any UHI may have developed. So what do you do?
1. You can do nothing, and leave the possible UHI in there.
2. You can have faith in your statistical algorithms, and use them to sniff out the UHI (this is what USHCN does now, and GHCN will be doing it soon, see Menne 2009, Pairwise Homogenisation)
3. You can say “I think #2 is too hard to do well”, and just toss the station out, at all times. After all, without doing the sort of work required for #2, you won’t really know when any UHI started. So you don’t know which data you can keep, so don’t keep any of it. This is essentially what GISS does.
I personally prefer #2, if the algorithm can be shown to be effective against test data. But if you don’t trust #2, then #3 is where you go.
Robert (10:34:27)
Ummm … here’s my full quote:
Since you have not advanced a single argument, I can only assume you don’t have an answer either … so why on earth would you attack me for not having one?
Well, if I had said what you quote above, I suppose you might have a point. Since I didn’t, you’ll have to be open to the idea that you’re attacking the wrong guy. If you want to be taken seriously, you need to pay attention to who you are attacking.
Can’t tell you how depressed I am to hear that …
Wise words from Willis:
“All that shows is that their errors roughly match, which is meaningless.”
“Why should citizen scientists like myself have to dig out these oddities?”
“The adjustments for each station should be published and graphed.”
I’d be curious to see your take on Agassiz, British Columbia, Canada maximum-temperature adjustments sometime Willis. If/when you have time to take a look, be sure to compare the max-Ts & their adjustments with the minimum-temperatures & their adjustments. I haven’t yet made time to sort through the mess, but even upon a quick glance it was evident that some “funky” assumptions have been made. This has consequences for some of my lines of local work (which I have shelved until I have time to patiently & carefully resolve the data issues).
Mr Eschenbach,
I didn’t read all the bazzilion comments so may be you have already been congratulated on your comment on the competence of the “homogenizers” to treat milk. (If I had been drinking milk when I read that I’m sure it would have come out my nose!) However, these “adjusters” are not homogenizing they are “pasteurizing” for which they seem most competent. Cooking the data killed the bacteria of information. Milk safe to drink again.
Will
My colleague just got back from the Bahamas sans a tan. I asked and he said it was “freezing” there (in the 50′ sF). Not a problem though….. I adjusted the temperature by 30F because of La Nina. So in my AGW calculations. I’m still using the adjusted value of 80F for the Bahamas last week. That’s a few tenths of a degree higher than normal so AGW is conclusively proven. That’s legitimate, right? I mean we all know the Bahamas should be warm right now!
In my copy of the GHCN data “Matanuska” seems to be spelled “Mantanuska”, and I have entries that terminate 1994, so am not really up to date. Never mind, the information is nevertheless quite interesting – provided it’s pertinent.
What it shows is that there was a large downward step at approx late 1944, followed by effectively stable conditions until the PDO shift of early 1976 caught up with Alaska in October 1976, when an large (1.2C) UPward step occurred. This was followed by a very slight decline until the end of my data, 1994.
Anchorage records show exactly the same step pattern, with step changes at the same dates.
Can someone point me at a complete Matanuska data set please? I can then readily update this commentary. However, I can’t say anything useful about data adjustment, so maybe I’m off topic.
carrot eater (11:14:24), thanks for your reply. Real questions are always interesting. You say:
More conservative? I find the GHCN method more conservative, but I’m not happy with either one.
None of the above. Fallacy of the excluded middle.
What I would do first is get actual data about Matanuska, including the total station history, and all of the photos that I could get, both current and historical. I’d get as much population data about the surrounding area, including total economic activity (since McKitrick has shown this to be a factor). I’d look at all of these plus the temperature record, and see which if any of this seems to be affecting the temperature record.
Then, I’d compare the station to the surrounding stations to see if I could identify a breakpoint, as is done in the GHCN/GISS analyses. However, I would not (as they do) just take that as gospel. Often, the results of these types of algorithmic comparisons are complete nonsense.
Matanuska is a great example. From 1920 to 1970, they decreased the apparent warming. From 1970 on, during the time when we might suspect UHI, they increased the apparent warming. I’m still waiting for someone to explain how that makes any sense at all. If there was UHI, you’d want to reduce the apparent warming, not increase it as they have done.
And why reduce the early warming? So what if surrounding stations were different from Matanuska? Nature is not homogeneous. If it were, we’d only need one station. Without a theoretical reason of any kind to back it up, changing the early data cannot be justified.
Neither you nor anyone has explained a logical reason for the adjustments, either warming the early data or cooling the later data, and without that, why should we trust the algorithm?
In the end, as Matanuska proves, it is obviously not something that we can leave to a computer to decide. So at the end of the day, I would weigh all of the evidence, and make a decision, what ever that decision might be. Don’t touch it, adjust it, throw it out, whatever. I would then document the decision, and list all of the reasons for each change to the record. I would graph the exact effect of my choices on the record. I would publish all of this in a clear, readable, understandable format, so that if someone thinks I’ve made a mistake, they can find it and fix it.
That’s what I’d do. So … neither #1, #2, or #3 …
Thanks,
w.
Willis Eschenbach (10:39:18) :
And one more thought:
“But given that Matanuska and Anchorage are only 30 miles apart, what is the explanation for the huge difference in the two adjustments?”
You’re looking at it backwards. The adjustments will be whatever they need to be, in order to get the long-term trends to be the same at the two stations (meaning, to get them both to match that common set of surrounding rural stations). Or at least, as close as you can match, with the two-legged adjustment and the data you’re working with.
So if you want to see if the adjustment was successful in what it was trying to do, overlay the two adjusted series, and see how well the trends match. Don’t compare the adjustments themselves.
I keep forgetting Alaska is outside the USHCN.
Robin Edwards (11:29:58)
That is the complete dataset. And you are correct about the effect of the PDO on Alaska temperatures. See my 2004 analysis of the PDO and Alaskan temperatures here. It’s another of the things to consider when making adjustments, I should have mentioned it in my reply to carrot eater at Willis Eschenbach (11:39:56).
Willis Eschenbach (11:39:56) :
“More conservative? I find the GHCN method more conservative”
Just thinking about it, I disagree. I think GISS is more aggressive in removing UHI, whereas GHCN is more precise.
At least for comparing GISS to USHCN using actual data (back when USHCN still had a separate UHI step), Hansen et al (2001) shows me to be correct; GISS is more aggressive in removing UHI.
As for comparing GISS to GHCN using actual data: let’s hold off for now. I have zero motivation to study GHCN adjustments, when I know they’re probably overhauling it for v3.0 later this year anyway.
“What I would do first is get actual data about Matanuska, including the total station history, and all of the photos that I could get, both current and historical. I’d get as much population data about the surrounding area, including total economic activity (since McKitrick has shown this to be a factor). I’d look at all of these plus the temperature record, and see which if any of this seems to be affecting the temperature record.”
Willis, this is not a serious option. You’ll never collect enough historical metadata to be able to explain every divergence between each station and its neighbors. And even if you could, it’d still be largely best to use the statistical methods (Menne’s new homogenisation, for example) to remove the artifacts. A picture could tell you that a tree was covering the station at a certain angle, and then it was cut down, but that in itself won’t tell you how big an adjustment to make.
Finally, if GISS used historical metadata this closely, the adjustment method would no longer be objective. It would not be reproducible, as it would require some human judgments. If you remember from Darwin, this is an issue in the Australian BoM adjustments. One student did some adjustments using all the historical metadata, but the next person couldn’t easily repeat it.
While some oddball stations might be bestowed with a weird adjustment, overall I think objective methods are both more reliable and preferable, for reasons of reproducibility.
Thinking a bit more about the method GISS is using, that I described above, . The plots Willis has shown are just approximators to the difference between the “urban” station and the weighted average of surrounding “rural” stations. If you think there’s something extreme about them, it’s probably not in the adjustment arithmetic – it reflects the actual behaviour of that difference. There’s nothing particularly bad about a LS fit of a bent two-line segment.
So what does the adjustment achieve? Suppose you put more effort into getting a better approximator to the difference. In fact, you could just use the exact difference. That would completely replace the urban station result with the average of surrounding rural. The eventual global trend would then be just provided by rural stations. That is good if you have enough of them.
So why include the urban stations in the calc at all? They have an odd effect – by putting them in, and then replacing them by the average of local rural stations, you upweight the effect of those local stations in the global average. This probably wouldn’t matter much, and the effect is further muted anyway by the gridding.
The piecewise linear approx leaves a small residual effect of the urban stations in the global average, but only contributing short-term variation. The trend effect has been removed. And since the short-term effects are almost totally lost in the averaging, it seems to me that there’s little difference between GISS UHI adjusting and leaving out urban stations completely.
Paul Vaughan (11:21:58) : edit
Well, here’s a start on Agassiz, I just figured out how to make a blink comparator …
Willis, I’ve just looked at your 2004 paper on Alaskan climate, and enjoyed it a lot. Just one thing, though! I note that you seem always to use 17 year Gaussian smoothing. I’m a strong believer in eschewing the salve of smoothed data, which I tend to equate with trying to make things easier for journalists and politicians. OK, we know their limitations, so I suppose that’s a justification. However, when you look carefully at the individual month data, a somewhat different picture emerges. First, as you know, it is very useful to “deseasonalise” the monthly data, by subtracting the overall month averages from each item. Next, form the cumulative sum of the deseasonalised data, and plot this against the time base. What happens might be an eye opener. The steady increase that your plots show over the period from around 1970 to perhaps 1985 now becomes a step change that takes place over one or two /months/, and which is preceded and followed by periods of remarkable stability The PDO itself underwent the same step change, but a few months earlier.
This sort of underlying behaviour renders the conventional “trend line”, routinely computed, without consideration of its real-world implications, a misleading concept. My approach is to let the data themselves guide one’s thoughts about fitting a linear model. The notion that a linear fit must be “safe” is not one to be applied without due thought in the realm of climate science. Good though the human brain is at spotting patterns in data plots it may not be good enough!
I am pretty sure that most methods currently used for handling climate time series over periods of perhaps a couple of centuries down to a few years tend to disguise the occurrence of step changes. This simplifies potential explanations but may be hiding something of fundamental importance.
Over the last 16 years I have looked at several thousand series, of many types, and am convinced that abrupt change is the norm, rather than the exception. What you are likely to notice is that once the position of a potential step has been suggested your ideas about the linear fit, and indeed the process of smoothing, might need some modification.
I would like to be able to show a few GIFs, in this thread, but don’t know how :-(( They would lend some weight to my words.
Robin
carrot eater (12:04:14), thanks for your thoughtful reply. You say inter alia
There are mathematical methods which can identify a breakpoint in a dataset without reference to its neighbors. If we know a tree was cut down in 1938, and we find a breakpoint in 1938, we can make a reasonable adjustment for the change based on the math.
Next, I have no problem with someone making an adjustment based on a change, as long as they clearly identify the amount and the reason for the adjustment. It does require human judgement, but that is true in many parts of science. As long as they can specify their reasons, we can see if they are reasonable.
Next, I do not find “reproducibility” to be a valid reason for selecting an automated method. If it is making bad adjustments reproducibly across a variety of stations, that’s not a good outcome.
Finally, if you do use an automated method as GISS and GHCN do, it is imperative that you go through and toss out the adjustments that are clearly bogus. Adjusting Matanuska to increase the recent (UHI?) warming is one such adjustment. Quality control is a crucial part of any computerized adjustment system.
there should be some good coming out of all this Cherry (picked) Fudge; now there must be a market.
carrot eater (11:40:02)
I took a look at the period of overlap where there is no missing annual data (1938-1990).
Before adjustment, the long-term trend for Matanuska was -0.01°C per decade.
After adjustment, the long-term trend for Matanuska was -0.01°C per decade.
Before adjustment, the long-term trend for Anchorage was 0.18°C per decade.
After adjustment, the long-term trend for Anchorage was 0.07°C per decade.
So I’d say that your claim, that the purpose of the adjustments was to correct the long-term trends, didn’t work for Matanuska. Anchorage’s trend was reduced, but Matanuska’s was left unchanged.
I still haven’t heard any reason for increasing the recent Matanuska trend, or reducing the earlier Matanuska trend. Why should it match its neighbors? Nature doesn’t work that way, there are differences between stations.
Using nightlighs only for judging urban/rural status of station is risky, since the coordinates of many stations are not exact enough for this.
I’ve looked into this for Swedish stations in the GHCN, and there are several significant errors. Härnösand for example is a fairly large town, but the coordinates are off by a few miles, placing the station in the middle of an uninhabeted forest area. Same thing for Halmstad where the station is at the airport on the outskirtsof the town, but the coordinates places it in the middle of a field several kilometers east of the airport.
In some cases the coordinate errors may even be deliberate. I suspect this is true for Jokkmokk where the station is at an Air Force Base whose position and even existence was secret until the recce satellite era. Here the coordinates are off by more than 10 km.
Re: Willis Eschenbach (Feb 22 12:50),
Why should it match its neighbors?
I think this is the wrong way to look at it. Matanuska is being defined (maybe wrongly) as urban, and is being replaced by an average of it’s rural neighbors. The adjustment curve you have plotted is close to what is required to do this.
Since the neighbors were already contributing to the global average, the nett effect is that M is simply being left out, with some minor changes to the weighting of the nearby rural stations in the mix.
Willis Eschenbach (12:40:08) :
Well, once the crowd disperses, it’s apparently easier to have a rational discourse.
“There are mathematical methods which can identify a breakpoint in a dataset without reference to its neighbors. ”
In some applications, sure. In this one, I just don’t see how you can pull it off. You’ll find the breakpoints, but without the neighbors, you won’t know what should have been happening at your spot.
“Next, I do not find “reproducibility” to be a valid reason for selecting an automated method. If it is making bad adjustments reproducibly across a variety of stations, that’s not a good outcome.”
I think it’s quite important, and I think the nature of WUWT testifies to it. People don’t trust adjustments that they can’t dig into for themselves. Having a lot of non-objective adjustments will just become a huge black box that nobody here could really examine, and it would only lead to yet more controversy.
Also, I do not think you’ve demonstrated that the adjustments are on the whole bad.
“Adjusting Matanuska to increase the recent (UHI?) warming is one such adjustment.”
Again, I strongly encourage you to finish the job you started, and analyse all the rural stations that serve as the reference set here. The work is very incomplete without it.
The GISS UHI adjustment often does put in an increased warming trend, but that could be for some good reason. Say Matanuska had some station move or somesuch that messed with its trend, such that it didn’t match well with the neighbors. The UHI adjustment will step in and so something about it. I agree it’s crude, so I prefer the NOAA methods. But because of this, I wouldn’t just toss out any positive UHI adjustment out of hand; it could be positive because it’s correcting for something else.
This is assuming the rural reference stations aren’t themselves garbage.
“Robert (10:34:27) :
“No – it is the AGW fantasists that need to do that, actually.”
You’re wrong, Willis. You’re ”
Robert is amazing. After repeatedly misrepresenting other people’s statements, he now uses outright wrong quotes. I got suspicious because “AGW fantasists” is not Willis’ style. Robert, are you such a helpless tw*t that you need to do that or are you just extremely careless?
Willis Eschenbach (12:50:13) :
“I took a look at the period of overlap where there is no missing annual data (1938-1990).”
You should try the comparisons on either side of the Matanuska dog-leg in 1970.
“I still haven’t heard any reason for increasing the recent Matanuska trend, or reducing the earlier Matanuska trend. ”
Because you haven’t looked for the reason. Please just look at the entire set of neighboring rural stations.
“Why should it match its neighbors? Nature doesn’t work that way, there are differences between stations.”
And yet, nature does work that way to a pretty good extent. You can see how good the correlation is between Anchorage and Matanuska, even with any UHI still in there. Anomalies correlate pretty far out, both in trend and in the variance. This has been well demonstrated, and if you disagree, you’ll have to do much more than just saying you don’t like it.
Nick Stokes (12:58:09) :
That is exactly correct. Though, if the Matanuska record was really messed up for some reason, it actually wouldn’t be possible to give it the same trend as the average of the rural neighbors. At least not by the GISS method.
But somebody needs to come up with a distance-weighted average of rural neighbors, to provide the comparison set.
This is what we’ve needed the entire time.
tty (12:56:17) :
There was an amusing article recently where the authors tried to use Google Earth to look at the stations, using the coordinates. They also found that the coordinates were not perfectly precise.
Willis,
This is looking like a typical Eschenbach effort. You scour stations to find something that looks a little odd. You write a post under the heading “Fudged fevers”. You say you don’t understand it.
But GISS publish the data that they use, and the algorithms and the code. Other independent groups have used and emulated the code, getting essentially the same results, so GISS are using the code that they publish. So if you’re going to persist with accusations of fudging, you might at least try to show where they do that in the code. I’ve pointed out the relevant parts.
Nick Stokes (14:17:52) :
My complaint as well. “Fudged” is in the title; Willis says he can’t think of what could justify x, y or z. When in fact everything is wide open and transparent. Perhaps Willis doesn’t agree with how the algorithm works, but he owes it to the readers to at least tell them what the algorithm is, or even that one exists and where to find it. It’s an objective method, so there’s no specific fudging. What you can then do is look at the surrounding stations and decide whether the algorithm churned out anything reasonable.
Though I wouldn’t explain it from the code primarily; just point to the papers. They’re rather easier to read. GISS papers aren’t paywalled, so nobody has that excuse to not even look at them.
Nick Stokes (14:17:52)
Starting out with ad hominems doesn’t do your cause any good, Nick. I didn’t “scour stations”, I looked at one station, so that’s a lie. Starting out with a lie is a curious way to establish your credentials.
I said I didn’t understand why Matanuska had been adjusted down and then back up again.
So far, nobody has given me a reason. Including you. I’m sorry, but “all the neighbors do it” doesn’t work for me. So what? That’s what makes for horseraces.
If you are so insightful about all this, please explain why.
First, we’ve gone over the “others get the same results” question above.
Perhaps my use of the word “fudged” is confusing you. I meant it in the sense of “pushed around without reason”. If you have those reasons, bring it on.
You keep insisting that perfectly good data should be changed purely because a computer says so. I ask why, and you point me toward the computer code … miss the point much? I know what the code says, I was likely writing computer programs before you were born. I’m questioning the whole procedure, not the details of how they code a program to shoehorn all the local stations into a “one-size-fits-all” straitjacket. Yes, you can do it, and yes, the computer codes do it.
But until someone comes forth to explain a reason for adjusting Matanuska down for fifty years and up for twenty years, I’m going to continue to ask the question. If you want to blindly adjust what appears to be perfectly good data just because some computer orders you to do so, that’s your choice.
Me, I ignore alien orders. I won’t adjust until you can explain why they should apply to Matanuska. I’ve asked that again and again, and neither you nor anyone else has answered the question. When you have the answer, come back and tell us. Until then …
carrot eater (14:33:39) : edit
You, like Nick, are missing the point. I know how the data was fudged. It was fudged by a computer algorithm, one that obviously doesn’t work well.
What I don’t understand is how this is all justified. I keep asking for a reason that anyone would start adjusting a pristine rural record in 1920. Do you or GISS have the slightest scrap of evidence that there was something wrong with the record?
Because if you don’t, then the data is fudged. Fudged by a computer, using a known and documented program … but fudged nonetheless. When you change data without a reason, merely because you were ordered by the almighty Computer to do so, I call that fudging. Don’t like it? Provide the reason for the adjustments made to Matanuska. We know the method, which is to force them to agree with their neighbors. But what is the reason to fudge the data that way? That’s what I don’t understand.
Which is what I said at the start.
DCC (09:06:36) :
@Alan S (15:55:04) :
“I have extreme difficulty understanding, from GISS policy as related above, why any rural station would be adjusted upwards. I assume I am missing something obvious and would dearly like to be enlightened.”
“I had trouble with that, too, until I realized that they are doing things a bit backwards. They are assuming that the highest urban readings, which include UHI, are the correct temperature. Therefore the rural temperatures need to have UHI added in. It’s a very peculiar way to do things. One would think that you should subtract the UHI out. Maybe they just like high numbers!”
I was hoping that I had missed something fundamental, so basically the surface temperature record has been hi-jacked.
It is thoroughly depressing to see.
I assume that is why Trollbert and Pia Carrot are currently spinning like a tops to obfuscate, re-direct, misquote and if that fails out right lie, to try to muddy this thread.
When I saw the PDO hit the deck @ 1998/1999, re conned, ” That can’t be good”, then we had the Solar minimum dragging on and on, then we had the Argo Buoys reporting sea temperatures dropping and now we have an air effect the AO going negative.
It is looking like time to buy shares in companies who make down clothing and get kitted out early before next winter.
“It’s worse than we thought”, perhaps?
carrot eater (13:19:08) :
Though, if the Matanuska record was really messed up for some reason, it actually wouldn’t be possible to give it the same trend as the average of the rural neighbours.
If the data comes from the experimental farm (see my earlier post) then I am not so sure just how much more “rural” the Matanuska site could be. If the farm is the source then adjustments should really only come from the instrument/site change side. That is to say not from UHI. Pop. 1917=0 Pop. 2010=0.
(stay with me carrots) Nick Stokes (14:17:52) :
(…) So if you’re going to persist with accusations of fudging, you might at least try to show where they do that in the code.
I get the feeling that that is exactly where this (suspicious changes) will have to go. The answer from the pro’s seems to be “it’s in the PR literature”. Fair comment. BUT as with the NZ Parliament asking for the details of changes to individual stations, the question is met with … ??? (I suspect this will be a global answer)
My view, currently, is that … Yes, everything is in the literature somewhere (at the macro level, in terms of the method(s) used on a particular run). No, nobody (CRU, NASA, NOAA…) could ever explain the micro details of Darwin or Matanuska (don’t get me started on Iceland!) as they are the by-products of the bulk processing algorithms used. The detail is lost in much the same way as it would be in processing a huge mail order address list.
Anyways … I think what needs to be done is a bit of debug output at each stage of the code we are allowed(!) to see (and run) or at least (PR) methods we can duplicate. Tracing Darwin or Matanuska at each stage may spread some light (groan) on why they are as they are.
Despite what the politicians and climateers say, the details are important. If you (CE and NS or even PJ and TP) cannot convince me (and presumably Willis) that Matanuska is a valid adjustment then we can never agree on the final result.
Willis – what, if anything, did you decide re: a surface stations type domain?
Re: Willis Eschenbach (Feb 22 15:22),
So far, nobody has given me a reason. Including you.
I have,here. Matanuska has been classified as urban, and is being eliminated from the trend calculation. The device being used is to add the difference between an average of nearby rural values and the M values. If that is done exactly, M has gone completely. It’s done approximately using this piecewise linear fit, to preserve some short term information. I don’t think that is much of a gain; it will, have very little effect at all.
The down/up that you complain about is not artificial. It’s the observed discrepancy between M and its rural neighbors. Whether you add that discrepancy, removing the effect of M, or just omit M, has the same effect.
“I was likely writing computer programs before you were born.”
I wrote my first computer program in 1964, using Manchester Autocode.
A bit of osbcure and unintentionally funny: Being that I am from that area (Kenai/Soldotna) I grew up drinking Matanuska Maid Homogenized Milk…
On that note, Matanuska did have a relative population boom, but exact siting of the area needs to be figured. Alaska is BIG, most areas can eat up a large population without much impact.
So, unless we do a Surface Station run down of it, we really don’t know. I didn’t see any links to it on the surface stations site, so…
If I end up back in AK for some work (pending) I can swing up and check it out.
carrot eater (13:13:49)
You are quite correct, that some “anomalies correlate pretty far out, both in trend and variance” … but some don’t. The present case is a perfect example. Although there is good correlation between Anchorage and Matanuska in terms of the variance, the trends are radically different. Anchorage has a trend of almost two degrees per century, while Matanuska shows no trend at all. Go figure …
You are taking a general observation (nearby stations tend to correlate) and trying to make it into an absolute (therefore if they don’t correlate, we are justified in forcing them to correlate).
To take another example, yes, members of a family tend to look alike too … except when they don’t. Their body measurements tend to correlate just like nearby temperatures. But we wouldn’t dream of “adjusting” the body measurements of the one solitary tall thin member of a family to match the measurements of a dozen of his shorter, heavier relatives. So how can you justify doing the same to temperatures? If you are looking for some mythical Nature without sports and freaks and oddballs and things that break the rules, you’re on the wrong planet.
Dominic Marcello (09:26:43) :
Matanuska station has not just sat in the same place since its inception. Its moved around a bit.
Good catch but I’m not convinced that the moves account for the GISS end. As far as I understand it GISS uses GHCN as a base set but replaces GHCN records with USHCN records where matches exist. USHCN (again, as far as I can see) has already adjusted for station moves and such. GISS may/not then attempt (as far as I can not/partialy see) to remove any adjustments made by the source parties (of GHCN/USHCN) to the GHCN(+/-)USHCN resulting set!
Is it any wonder Matanuska is a bustling metropolis – full of folk trying to escape a life chained to climate science.
Willis Eschenbach (15:29:36) :
“You, like Nick, are missing the point. I know how the data was fudged. ”
You are missing the first point. That your reader would have little idea of how the algorithm works, or what inspired it. Should the reader have to wade through a hundred comments before he finds a discussion of what the algorithm actually is, or even a statement that there even is an objective algorithm?
“What I don’t understand is how this is all justified. I keep asking for a reason that anyone would start adjusting a pristine rural record in 1920.”
I have told you the answer to that question. Maybe it was nice and rural in 1920, but their satellite data tell them it isn’t, now. Unless they implement a method like NOAA’s, they can’t figure out when any impact due to urban warming came about. So they essentially toss the station out. I would say this is a sign of being rather over-eager in trying to avoid UHI, if anything. I prefer the GHCN method, over GISS. Especially once GHCN v3.0 is implemented.
“Provide the reason for the adjustments made to Matanuska.”
It got flagged as urban, and that was the end of Matanuska. That’s all. I’d rather they use the NOAA methods and retain more information from Matanuska, but they instead choose to minimise the impact Matanuska as on the trends at the grid point.
“We know the method, which is to force them to agree with their neighbors. But what is the reason to fudge the data that way? ”
Again in GISS, the adjustment is done to remove the impact of the questionable station. As for GHCN, as has been shown very clearly, neighboring stations correlate very well with each other. So if there is a non-climatic influence/event/distortion at one station, you can get an idea of what the climate signal would have been, by looking at the neighbors.
“”Provide the reason for the adjustments made to Matanuska.””
By the way, even if you try to retain the station, you’ll never have enough historical metadata to track down the reason for every discontinuity. Especially outside the US. But a good statistical method will more-or-less correct the errors you know about, as well as those you don’t. It won’t be perfect, but nothing would be.
“It was fudged by a computer algorithm, one that obviously doesn’t work well.”
You keep saying that. You’ve done nothing to actually demonstrate this.
3×2 (16:11:49) :
To reduce the complication, I don’t think these stations are in USHCN. USHCN is a lower 48 thing.
Robert (10:34:27) :
Until then, I don’t think there’s anything left to say.
Please tell me that is in fact your last offer(ing)!
Nick Stokes (16:01:30) :
“to preserve some short term information. I don’t think that is much of a gain; it will, have very little effect at all.”
I feel the same way. I’ve always wondered if much is gained by keeping the short term variations.
Willis Eschenbach (16:02:02) :
Yes, in this case there is correlation in the variance, but the trends don’t match. So they ignore the trend at any station classified as urban. The end.
You should be happy; there’s not much chance of UHI creeping through if you just axe the urban station’s trend.
Of course, it isn’t perfectly axed, but it comes close enough.
Re: 3×2 (Feb 22 16:11),
USHCN (again, as far as I can see) has already adjusted for station moves
USHCN does not cover Alaska.
carrot eater:
Matanuska. The history of the area is well known. There was no development there before the war. The computer adjusted the temperatures during a time when there is no reason to adjust them. Therefore, the algorithm obviously doesn’t work well.
carrot eater (16:15:58) : edit
I assume that the reader … well … reads. If the reader reads, they would have read this in my original post:
So at that point they know there is an algorithm. As to what “the algorithm actually is”, that was beyond the scope of my article. I’m not interested in overly complicating my original post by trying to explain the intricacies of how the algorithm weights the nearby stations by using the formula
distance / -500 +1
If someone wants to know that, they can read further. But it’s far too much detail for my original article.
Don’t like it? Fine. You are welcome to write an article about how wonderful the GISS algorithm is, and all the details of its operation. Then you can explain how reasonable it it to knock three quarters of a degree off of a pristine rural record simply because (gasp!) it had a different trend than its neighbours.
Let me know when you do, post the URL up here, and I’ll come and ask questions. And I won’t attack you for not writing it the way that I would write it …